Climate Change Attribution Using Empirical ... - CiteSeerX

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The Open Atmospheric Science Journal, 2011, 5, 74-86

Open Access

Climate Change Attribution Using Empirical Decomposition of Climatic Data Craig Loehle1 and Nicola Scafetta*,2 1

National Council for Air and Stream Improvement, Inc., 552 S Washington Street, Suite 224, Naperville, Illinois 60540, USA 2

Active Cavity Radiometer Irradiance Monitor (ACRIM) & Duke University, Durham, North Carolina 27708, USA Abstract: The climate change attribution problem is addressed using empirical decomposition. Cycles in solar motion and activity of 60 and 20 years were used to develop an empirical model of Earth temperature variations. The model was fit to the Hadley global temperature data up to 1950 (time period before anthropogenic emissions became the dominant forcing mechanism), and then extrapolated from 1951 to 2010. The residuals showed an approximate linear upward trend of about 0.66°C/century from 1942 to 2010. Herein we assume that this residual upward warming has been mostly induced by anthropogenic emissions, urbanization and land use change. The warming observed before 1942 is relatively small and is assumed to have been mostly naturally induced. The resulting full natural plus anthropogenic model fits the entire 160 year record very well. Residual analysis does not provide any evidence for a substantial cooling effect due to sulfate aerosols from 1940 to 1970. The cooling observed during that period may be due to a natural 60-year cycle, which is visible in the global temperature since 1850 and has been observed also in numerous multisecular climatic records. New solar activity proxy models are developed that suggest a mechanism for both the 60-year climate cycle and a portion of the long-term warming trend. Our results suggest that because current models underestimate the strength of natural multidecadal cycles in the temperature records, the anthropogenic contribution to climate change since 1850 should be less than half of that previously claimed by the IPCC. About 60% of the warming observed from 1970 to 2000 was very likely caused by the above natural 60-year climatic cycle during its warming phase. A 21st Century forecast suggests that climate may remain approximately steady until 2030-2040, and may at most warm 0.5-1.0°C by 2100 at the estimated 0.66°C/century anthropogenic warming rate, which is about 3.5 times smaller than the average 2.3°C/century anthropogenic warming rate projected by the IPCC up to the first decades of the 21st century. However, additional multisecular natural cycles may cool the climate further.

Keywords: Attribution, climate change, ENSO, LULC change, solar activity, UHI effect. 1. INTRODUCTION There is little doubt that the Earth has warmed since 1850, the time when global instrumental temperature estimates are first available (Brohan et al. [1]). A significant increase in the warming has been observed since 1970 relative to the period from 1940 to 1970: see Fig. (1A). This sudden change in the warming trend has suggested an alarming anthropogenic effect on climate (IPCC [2]). However, partitioning causation has proven to be problematic. It is important to establish the relative importance of forcing factors in order to properly calibrate climate models used to project future climate scenarios and, in particular, it is necessary to determine whether multidecadal climatic patterns may be induced by natural multidecadal cycles. Attribution studies are based on either statistical or simulation approaches, or a combination of these. Several studies (e.g., Andronova and Schlesinger [3]; Crowley [4]; Damon and Jirikowic [5]; Hoyt and Schatten [6]; Lean et al. [7]; Scafetta [8]; Serreze et al. [9]; Stott et al. [10]; Tett et al. [11]) have arrived at divergent estimates of anthropogenic forcing. One reason for these divergent results is that the *Address correspondence to this author at the Active Cavity Radiometer Irradiance Monitor (ACRIM) & Duke University, Durham, North Carolina 27708, USA; Tel: 919-660-5252; E-mail: [email protected] 1874-2823/11

computations are poorly constrained. Each factor considered is uncertain both in terms of forcing and in terms of data, as documented by Scafetta [8]. For example, while greenhouse gases such as CO2 and CH4 no doubt have a warming effect and sulfate aerosols produced by volcanoes or industrial emissions no doubt have a cooling effect, the IPCC [2] itself acknowledges that there are large uncertainties as to the magnitude of their effects on climate. The same can be said of brown clouds (warming), black soot (warming), solar activity, and other factors. In none of these cases is a precise global dataset for the past 160 years available to provide input to an attribution study (Kiehl [12]), nor is the precise forcing known independently of attribution studies (e.g., Visser et al. [13]). For example, the IPCC’s best estimate of the climate sensitivity to CO2 doubling is 3 +/- 1.5 oC, although the distribution is skewed and has a tail to 10 oC (see figure 9.20 of IPCC [2]). Other climate mechanisms in climate models are also either poorly modeled (cloud cover, water vapor, ocean oscillations) or highly uncertain (land cover). More spatially resolved climate simulations (e.g., Wyant et al. [14]) produce cloud and water vapor patterns that differ from those in operational climate models. These differences are difficult to resolve within the traditional attribution study framework.

2011 Bentham Open

Climate Change Attribution Using Empirical Decomposition of Climatic Data

To overcome this problem we apply an empirical decomposition method that does not depend on assumptions about forcing magnitudes, on detailed historical inputs of the various factors, nor on climate models. Our approach is to empirically characterize the pattern of global temperature change and to relate this pattern to the historical timing of anthropogenic greenhouse gas forcing. 2. DECOMPOSITION ANALYSIS Long-term climate trends and cycles have been detected in many geologic datasets. The most regular of these are the repeated cycles of glaciations evident in ice core data (e.g., Rasmussen et al. [15]), though the precise factors governing this cycle are not fully worked out. A number of studies claim to have found decadal to centennial periodic behaviors in climate records that can be associated to solar cycles (discussed later). As well, the 11 and 22 year sunspot cycles appear to result from the effect of planetary tidal forces on the sun (e.g., Bendandi [16]; Hung [17]). These findings offer the possibility that decadal to centennial scale climate could have some structure in the absence of human activity, rather than being featureless. This possibility is not ruled out by the IPCC [2] because no scientific consensus exists on the importance of solar forcing and the current level of understanding associated with it is stated by IPCC to be low. Using the pattern of perturbations of the sun’s location from the center of mass of the solar system as a measure of the oscillations of the sun-planets system due to gravitational and magnetic interactions, which can be back-calculated by orbital calculations to any desired length of time, Scafetta [18] showed via spectral analysis and other means that two particularly strong periodic signals occur with the periods of about 60 and 20 years. These oscillations are synchronous in both astronomical records and numerous global surface temperature and climate records. Spectral decomposition of the Hadley climate data showed spectra similar to the astronomical record, with a spectral coherence test being highly significant with a 96% confidence level. A model based on these astronomical cycles fit the global temperature data well and fit the ocean temperature data even better. On the contrary, the spectral patterns of climate model simulations did not match those found in the climate at all (just a 16% confidence level), suggesting that current general circulation models adopted by the IPCC do not reproduce the climate oscillations at the decadal and multidecadal scales (Scafetta [18]). The physical mechanisms responsible for the cycles are not understood yet and, therefore, are not included in the current climate models. However, it is reasonable to speculate that planetary tides and solar angular motion affect solar activity (Scafetta [18]; Wolff and Patrone [19]). Changes of solar activity then influence the Earth's climate through changes of direct total solar irradiance, the sun’s magnetic field and the solar wind. Very likely, solar activity changes influence also the intensity of cosmic rays reaching the Earth, the electrical physical properties of the terrestrial magnetosphere and of the ionosphere. The latter influences the generation of clouds which modulate terrestrial albedo and, consequently, the climate (Kirkby [20]; Tinsley [21]; Svensmark et al. [22]). The above empirical relationship, even if the exact mechanisms are not understood yet, offers

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an opportunity to detect the anthropogenic signal as a residual component not explained by natural cycles. In what follows, we empirically estimate the structure of the temperature history data over a 160 year interval and evaluate the components of the empirical model as well as the residuals to assess natural vs anthropogenic forcings. A key to the analysis is the assumption that anthropogenic forcings become dominant only during the second half of the 20th century with a net forcing of about 1.6 W/m2 since 1950 (e.g., Hegerl et al. [23]; Thompson et al. [24]). This assumption is based on figure 1A in Hansen et al. [25] which shows that before 1970 the effective positive forcing due to a natural plus anthropogenic increase of greenhouse gases is mostly compensated by the aerosol indirect and tropospheric cooling effects. Before about 1950 (although we estimate a more precise date) the climate effect of elevated greenhouse gases was no doubt small (IPCC [2]). The approach we take is illustrated by the work of Klyashtorin and Lyubushin [26] who identified a periodicity of about 60 years in long-term climate data and fit a model to the global anomaly data, with good results. A problem with their model is that it is fit to the period in which we wish to detect a difference from any natural cycles of climate. A better approach would be to fit the model to the period prior to elevated greenhouse gases and then extrapolate it forward and determine whether the forecast climate pattern corresponds to the observed one. In fact, in the latter case the model would be tested on its forecasting capability. As explained above, we follow Thompson et al. [24] and IPCC [2] in identifying 1950 as the approximate year after which an anthropogenic signal appears to dominate climate forcings. Hegerl et al. [23] identified 1960 as the cutoff year, but their analysis might have found an earlier year if sulfate aerosols were not included in it. Given the strong periodicity in the solar signal detected by Scafetta [18], we used this model, with two cycles of lengths 60 and 20 years, plus a linear term. A free fit of the Hadley HadCRUT3 global surface temperature data found cycles quite close to this, but we use the Scafetta solar periodicities plus a linear trend. The linear trend would approximately extrapolate a natural warming trend due to solar and volcano effects that is known to have occurred since the Little Ice Age, a period that encompassed the Maunder and Dalton solar minima of the 17-19th centuries (Eddy [27]; Scafetta [8]). As explained in Scafetta and West [28] and in Scafetta [8] most of this warming is likely due to the increased solar activity with the volcano effect playing only a minor effect if recent paleoclimatic reconstructions of the global climate, which show a large pre-industrial variability (e.g. Moberg et al. [29]), are adopted. On the contrary, analysis using only Hockey Stick temperature graphs, which do not show a significant pre-industrial variability (e.g. Mann et al. [30]), suggest that volcanic and solar effects are equally important in explaining the Little Ice Age (Crowley [4]; Scafetta and West [28]). The model was fit by nonlinear least squares estimation using Mathematica functions, with phase and amplitude free but period fixed as above. The validity of this approach for estimating parameters for a time series model with data of

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Fig. (1). A) Model for entire period utilizing HadleyCRUT3 global surface temperature April 8, 2010 dataset. B) Residuals showing clear model mis-specification.

this length is demonstrated in Supplemental Information a. When fit to the entire 160 year period, the model

using the data only up to 1950 (Fig. 2A). See Table 1 for the evaluated regression coefficients in the two cases.

y (t) = A cos[2 (t-T1)/60] + B cos[2 (t-T2)/20] + C (t-1900) +D (1)

Just as in the solar data, the 60-year cycle has about three times the peak-to-trough amplitude (~0.24ºC) of the 20 year cycle (~0.08 ºC). The model fit is R2 = 0.53. Comparing the residuals over the entire period depicted in Fig. (2B), they appear stationary up to 1950, but there is a visible positive trend from 1950 to 2010. The residual warming observed since 1950 is mostly induced by an increasing anthropogenic

performs only decently (Fig. 1A), as the nonrandom residual signal over the entire period (Fig. 1B) reveals that the model is deficient. Since this mis-specification could result from not including anthropogenic effects that could have accelerated the warming since 1950, the model was refit



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Fig. (2). A) As in Fig. (1A), with model fit to pre-1950 data. B) Residuals. Before about 1950 residuals are stationary around the zero level. After about 1942 there is a clear upward linear trend which may be associated to anthropogenic warming.

warming signal due to global industrial development, land usage change and urban heat island (UHI) effects since 1950. A linear fit to the residuals since 1950 (Fig. 2B) has a slope of 0.66 +/- 0.08ºC/century (R2 = 0.59) and begins (>0) in 1942. Note that given a roughly exponential rate of CO2 increase (Loehle [31]) and a logarithmic saturation effect of GHG concentration on forcing, a quasi-linear climatic effect of rising GHG could be expected. In any case, this upward

trend is the result of all positive and negative additional climatic forcings (e.g., the anthropogenic ones) and other possible contributions (e.g., poor adjustment of the temperature data) that are not implicit in our model beginning in 1942. Fig. (2A) shows that the model fit from 1850 to 1950 reproduces the modulation of the temperature up to 1942 and has random residuals over this period (Fig. 2B). It is very


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unlikely that the 60 and 20-year cycles would match the pattern of temperature over this period by chance; thus, the empirical model calibrated on the temperature data before 1950 has the capability of forecasting the multidecadal oscillations of the temperature after 1942, which we do next.

maxima around 2000, as Fig. (3B) shows. These cycles would imply a combined warming of about 0.32oC on the total observed warming of about 0.5oC since 1970. Thus, about 60% of the warming observed since 1970 can be associated with natural 20- and 60-year multidecadal cycles.

Table 1.

3. MULTI-SECULAR EVIDENCE OF A QUASI 60YEAR CLIMATIC CYCLE

A

Regression Coefficients of the Harmonic Model Plus Linear Trend Depicted in Eq. (1) Used in Fig. (1, fit 1850-2010) and in Fig. (2, 1850-1950), Respectively Case 1

Case 2

0.121 +/- 0.015 ºC

0.121 +/- 0.016 ºC

B

0.034 +/- 0.016 ºC

0.041 +/- 0.016 ºC

C

0.0042 +/- 0.0002 ºC/year

0.0016 +/- 0.0004 ºC/year

D

-0.297 +/- 0.013 ºC

-0.317 +/- 0.011 ºC

T1

1998.62 +/- 1.2 year

1998.58 +/- 1.3 year

T2

2000.98 +/- 1.5 year

1999.65 +/- 1.3 year

Note that the harmonic coefficients A, B, T1 and T2 are compatible within the error of measure. Parameter A is the 60-year cycle amplitude and B the 20-year cycle amplitude.

The above finding is also confirmed by two recent studies that show a similar linear anthropogenic warming trend after about 1950 (Thompson et al. [24]) and about 1960 (Hegerl et al. [23]. Both studies also find a small residual warming in the pre-1950 or 1960 period but do not attribute this trend to human influences. The magnitude of the Thompson et al. [24] residual post-1950 trend (i.e., the anthropogenic component) is similar to our result. These studies attempt to filter out sources of extraneous noise, such as ENSO events and volcanoes. They make many assumptions, which are not needed in the present study in which short-term perturbations are simply noise relative to the 60 year cycle and the long-term natural trend. To verify the combined effect of natural plus anthropogenic forcings, a full model was constructed, with the anthropogenic linear trend obtained by fitting the residual from 1950 to 2010 assumed to be in effect since 1942. This model (Fig. 3A) fits the entire 160-year record better than any model we have seen. Fig (3B) depicts the two harmonics at 60 and 20-year period and the linear natural trend (fit 1850-1950) added to the anthropogenic linear trend (fit of the residual in 1950-2010) since 1942. It would thus appear that the modeling approach used here has captured the essential features of the climate from 1850 to 2010. A model based on 100 years of data (from 1850 to 1950) shows stationary fluctuating residuals (Fig. 2B). The extrapolation period 1950 to 2010 shows an upward linear pattern of residuals, which we interpret as a signal of anthropogenic warming. The slope of this anthropogenic warming trend is 0.66°C/century since 1942. Fig. (3A, B) show that about 50 +/- 10% of the 0.8oC global surface warming observed from 1850 to 2010 is likely the result of a natural warming trend recovery since the LIA plus the combined effects of 20- and 60-year natural cycles. In particular, the sudden and alarming warming increase observed from 1970 to 2000 is mostly a consequence of the combination of the 20- and 60-year natural cycles. Both cycles were at their minima in about 1970 and at their

Here we list empirical studies supporting the existence of a quasi 60-year cycle in the climate system. A 20-year cycle with smaller amplitude will necessarily be harder to detect in geologic records, and is not evaluated here. Also, note that climate may be characterized by other cycles with periods longer than 60-year (e.g., Loehle and Singer [32]). Because complex interference patterns may emerge through the superposition of all cycles and volcano eruptions can further disrupt the signal, perfect 60-year harmonics may not be easily visible in the records. Noisy geologic data may not show a signal even if one is present. While spectral coherence of the Hadley historical data and solar data has been established (Scafetta [18]), does this effect extend before 1850? Before 1850 global instrumental temperature records are not available. Thus, it is possible to use only a few historical European temperature records dating back since the 17th century and several paleoclimatic records. These records may present several problems and may be a poor proxy for the real global climate. It has been recently noted that a 500 year temperature reconstruction in the Mediterranean basin (Spain, France, Italy) by means of documentary data and instrumental observations suggests a dominant ~60 year oscillation (Camuffo et al. [33]). Moreover, there are multidecadal periods of high and low ENSO-type activity evident in historical records going back several hundred years (e.g., Biondi et al. [34]; Mantua and Hare [35]; Mantua et al. [36]; Minobe [37-39]; Patterson et al. [40]; Shabalova and Weber [41]; Wiles et al. [42]; figure 5 in Gergis and Fowler [43]). The long-term ENSO-type signal has been shown to be quasi-periodic, with period of 50 to 70 years, in records such as the Greenland ice core, bristlecone pine ring widths, and sea sediment records of fish abundance going back thousands of years (Klyashtorin and Lyubushin [26, 44]; Klyashtorin et al. [45]). Over these long periods, cycles of 50 to 70 years were shown, using moving window spectral decomposition, to increase in strength over the last 600 to 1000 years, reaching a peak strength in the 20th century. Klyashtorin and Lyubushin [26] and Klyashtorin et al. [45] also showed that over the past 100+ years an average 60 year global temperature cycle is strongly coherent with the Pacific Decadal Oscillation (PDO), the zonal mode of the Northern Hemisphere Atmospheric Circulation Index, the Aleutian Low Pressure Index, flood levels in the Neva River (Russia), and precipitation in Oregon, among others. In addition, the ups and downs of major ocean fish stocks are almost completely explained by this ~60 year climate cycle (e.g., Klyashtorin and Lyubushin [26]; Mantua et al., [36]). The Atlantic Multidecadal Oscillation also closely tracks this approximately 60-year cycle (figure 2 in Levitus et al. [46]). Wiles et al. [42] found evidence for the persistent effect of the PDO on climate in Alaska over the past 1000 years. A quasi-60 year periodicity is found in secular monsoon



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Fig. (3). A) Full reconstruction model, which uses Eq. 1 (= natural modulation) fit from 1850 to 1950 plus the additional residual upward linear trend (=anthropogenic modulation) since 1942. B) Components of the model.

rainfall records from India (Agnihotri and Dutta [47]). Yousef [48] showed a good anti-correlation between ENSO events and the Wolf-Gleissberg cycle over 300 years. A clear quasi 60-year cycle is observed in the global sea level rise record since 1700 (Jevrejeva et al. [49]). A 50 to 80 year cycle over hundreds of years has also been found in sedimentary records in the northeast Pacific and their cause has been related to solar and cosmic ray activity cycles

(Patterson et al. [40]). Ogurtsov et al. [50] found strong evidence for the cycle, such as a 60 to 64 year period in 10Be, 14 C and Wolf number over the past 1000 years, which may indicate a solar origin. The solar system oscillates with a 60year cycle due to the Jupiter/Saturn three-synodic cycle and to a Jupiter/Saturn beat tidal cycle (Scafetta [18]). Fig. (4) depicts two multi-secular climate records showing multiple quasi-60 year large oscillations since 1650.


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Fig. (4). A) G. Bulloides abundance variation record found in the Cariaco Basis sediments in the Caribbean sea since 1650 [Black et al., 1999]. B) tree-ring chronologies from Pinus Flexilis [MacDonald and Case, 2005] as an index to the PDO. Both records show five large quasi 60-year cycles since 1650.

Fig. (4A) depicts the abundance of G. Bulloides (an indicator of surface temperatures) found in the Cariaco Basin sediments in the Caribbean sea since 1650 (Black et al. [51]): the best sinusoidal fit gives a period of about 61.5 +/4 years. This record, which correlates well with time series of solar output, is an indicator of the trade wind strength in the tropical Atlantic Ocean and of the North Atlantic Ocean atmosphere variability. Fig. (4B) depicts the reconstructed

PDO based on tree-ring chronologies from Pinus flexilis in the United States West Coast (MacDonald and Case [52]): the best sinusoidal fit gives a period of about 59.5 +/- 4 years. Thus, both records show a clear 60-year cycle. These cases negatively related to temperature due to the type of proxy.


A potential source of data for evaluating this cycle can be found in multiproxy temperature reconstructions (e.g., Moberg et al., [29]). Unfortunately, most of these reconstructions must be rejected as accurate data sources: in fact, paleoclimatic temperature reconstructions appear quite different from each other (North et al. [53]). Many rely on tree ring data, but it has been shown (e.g., Loehle [54]) that tree ring data, as well as other proxy records have issues that need to be resolved before they can be used to develop reliable climate histories.. In other cases, multiple proxies either are dated at wide intervals such as 100+ years, precluding detection of a multi-decadal signal, or suffer from large dating error, which in combined series will lead to a smearing of multi-decadal peaks (Loehle [55]). Given that this ~60 year periodic climate pattern has such a long empirical history with some evidence for links to solar activity and solar system oscillations, it cannot be attributed to human activity and cannot be simply random noise. Therefore, we are justified in factoring it out of the historical record in order to discern the human influence on climate. We next evaluate additional components that can be potentially factored out and, then, develop a final model that is extrapolated to 2100. 4. TROPOSPHERIC AEROSOLS A major factor often considered crucial for proper climate modeling is the influence of tropospheric aerosols that result from industrial activity, which are often assumed to have been a major cooling influence in the 1950s through the 1970s due to their reflective properties (although Schiermeier [56] argues that the aerosol extent and forcing data are shaky at best). However, because the climate appears to contain a ~60 year cycle for several centuries, much of the cooling may have been induced by this cycle, which was in its descending trend during this period. In fact, the residuals from the pre-1950 model (Fig. 2B) over the decades of the 1950s through 1970s show not a decline but a constant linear rise for the anthropogenic component, which is inconsistent with a strong cooling effect over this period. Our full model (including the linear anthropogenic effect since 1940) shows a cooling in the 1950s through 1970s without any forcing from tropospheric aerosols (Fig. 3A). Note that the amplitude of this 60-year cycle has been determined fitting the data before 1950. Moreover, the periods 1880-1910 (slight cooling) and 1910-1940 (significant warming) are statistically equivalent to the periods 1940-1970 (slight cooling) and 1970-2000 (strong warming), respectively. Thus, while tropospheric aerosols might have contributed to the cooling between 1940 and 1970, most of the observed cooling is likely associated with the 60-year cycle, which was in the declining phase during that period. 5. LAND USE EFFECTS ON TRENDS The final factor considered is the urban heat island (UHI) effect, including other land use/land cover (LULC) changes (e.g., Klotzbach et al. [57]). The UHI effect has long been known (e.g., Klysik and Fortuniak [58]). It results because urban structures have lower albedo, which raises temperatures, and retain heat, which raises nighttime minima. In addition, there is less evaporative cooling in a city. Herein, we discuss the current uncertainty relative to


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this phenomenon. It is not known how well the land surface temperature records are corrected for the UHI effect. The UHI effect arises not from the mere fact that a city is warmer than a rural site (e.g., Klysik and Fortuniak [58]), which is of no consequence for calculating trends, but from the effect at a given weather station location as the city grows around it, causing a gradual warming bias over time ([e.g., Böhm [59]; Magee et al. [60]). While many studies have documented UHI effects at the city level, few have done so at larger scales. The contribution of UHI effects at larger scales arises from the fact that a large proportion of weather stations have been established to serve cities and airports, with few in remote locations. Thus, UHI effects contaminate the instrumental temperature record. In a landmark study, Jones et al. [61] evaluated the UHI effect for China and found that 39.6% of the instrumental warming from 1951 to 2004 was contributed by UHI and related effects. In another set of studies, McKitrick and Michaels [62, 63] and McKitrick [64] showed that local warming (deviations from global trends) was proportional to national economic activity, a surrogate for urbanization, and that this effect is real and not caused by, for example, atmospheric circulation patterns. They estimated that about half of the land warming claimed by the IPCC to be UHI and land use related. In an analysis of trend differences between satellite and surface data, Klotzbach et al. [57] suggested that from 30 to 50% (based on UAH satellite data) of the instrumental land surface warming trend from 1979 to 2008 was spurious. Kalnay and Cai [65] found 0.27°C per century of the warming trend over the continental United States to be due to land use change effects. Other studies (e.g., Christy et al. [66]; Fall et al. [67]) support these results. The above studies suggest that the contribution of UHI warming to land warming is difficult to determine accurately and to filter from the global surface temperature record. The warming since 1950 might be lower than what is currently believed and the climate effects of the anthropogenic forcings may be overestimated also for this reason. Thus, the modeled upward warming trend attributions depicted in Fig. (3A) should be interpreted as an upper limit for an anthropogenic (GHG plus aerosol) contribution to climate change. 6. 21ST CENTURY CHANGE

FORECAST

OF

CLIMATE

Based on the analysis in this study, it is possible to make a preliminary forecast of future trends that may be reliable for the coming few decades. We believe this is possible because the natural variability may be assumed in first approximation to be a sum of a possible upward linear trend started since the Little Ice Age, which may still continue during the next decades although it is likely part of a millennial long natural cycle which includes the Medieval Warm Period and the Little Ice Age (see Loehle and Singer [32]), plus the periodic multidecadal component we have detected. That a natural trend may not exceed the above estimated upward linear trend from 1850 to 1950 may be indirectly suggested by the fact that since 1930 the rate of sea-level is slowing slightly (Houston and Dean [68]), which may suggest that a millenarian natural climate cycle is turning


Loehle and Scafetta

Fig. (5). Forecast models. Thick line is the global surface temperature. Dashed thick line is the total (natural plus anthropogenic) model reconstruction. Dashed light line is the continuation of the modeled natural component alone.

down. Interestingly, the measured deceleration of the sealevel rise from 1930 to 2010 occurred despite the strong positive acceleration of anthropogenic GHG emissions during the same period. Moreover, the anthropogenic component of the warming (GHG and aerosol forcings and/or LULC and UHI effects) seems to have resulted in a linear additional warming trend from 1942 through 2010, which matches the long-term climate model outputs (IPCC [2]). The combined (20 and 60-year cycle plus two linear trends) model depicted in Fig. (3A) matches the 160-year historical record exceptionally well. Thus, extrapolating the model another 90 years may be useful. The result (Fig. 5) is a continued warming with oscillations to a high in 2100 of about 0.6 °C above 2000 values. However, if part of the linear warming trend is due to UHI and LULC changes, the anthropogenic effect due to GHG and aerosol would be smaller. Moreover, the full anthropogenic effect of 0.66 ºC/century would be masked by the natural multidecadal 20 and 60-year cycles until 2040. Note that for the full model the downturn in temperature evident in the satellite observations after 2000 (Loehle [69]) is present. This downturn in the temperature since 2000 was not reproduced by any projection of the climate models adopted by the IPCC [2] although it is predicted by the celestial empirical model of climate recently proposed by Scafetta [18]. The above estimate of a possible warming of only 0.6 °C above 2000 should be compared against the IPCC projections that claim a monotonic anthropogenic warming with an average global value in 2100 of about 3°C above 2000 values for the likely scenarios and even more for the upper bound scenarios.

7. CAN SOLAR ACTIVITY EXPLAIN THE TEMPERATURE MODULATION? If anthropogenic forcing has been overestimated by the models adopted by the IPCC, it is necessary that other natural forcings such as the solar forcing have been underestimated by the same models to balance the secular warming trend observed in the temperature record. Herein we investigate whether solar activity can explain a quasi 60-year modulation of temperature and at least part of the upward trend by expanding the discussion presented in Scafetta [8]. The task is not simple, because past total solar irradiance (TSI) variation is not known with certainty (Supplemental Information b). We develop an enhanced version of the Scafetta [8] model in Supplemental Information c, which uses the TSI record as a proxy for determining a phenomenological signature of the total solar effect on climate. This model empirically evaluates the climate sensitivity to solar changes and, therefore, it automatically takes into account natural amplification mechanisms such as those due to cloud and GHG feedbacks. Figure Suppl. (2) in Supplemental Information c shows that the proxy model (green) presents a slight increase between 1850 and 1880/90, a decrease from 1880/1890 to 1910, an increase from 1910 to 1940/50, a decrease from 1940/50 to 1970/80, and an increase from 1970/1980 to 2000, and a decrease after 2000. Thus, it shows a quasi 60year cycle, which is clearly present in the gravitational oscillations of the solar system (Scafetta [18]). The latter 30year pattern since 1980 is perfectly compatible with the ACRIM total solar irradiance satellite measurements that show an increase from 1980 to 2000 and a decrease from 2000 to 2010 (Scafetta and Willson [70]). This is an


important result because some solar scientists currently believe that the ACRIM trend is instrumental and not of solar origin (Fröhlich [71]; Lockwood and Frohlich [72]). From the above, it appears that these TSI proxy models can approximately reproduce the quasi 60-year modulation of the temperature. In addition, they contain an upward trend that is part of a multisecular solar cycle responsible for much of the cooling from the Medieval Warm Period to the Little Ice Age and the warming from the Little Ice Age to the current warm period. To better show this we applied the empirical temperature model of Scafetta [8] to the novel green TSI proxy curve and to the average of the three TSI curves represented by the black curve in the Supplemental figure to obtain values for forcing. We plotted the two solar phenomenological signatures in Fig. (6) against the global surface temperature record. Fig. (6) confirms that there is good agreement, especially after 1900, between the empirical ~60 year modulation of temperature (Fig. 3A, thick line) and the climate signature that would be imprinted by solar multidecadal variation. If the correspondence is not rigorous it is because the available TSI proxy models can only approximately reproduce the real multidecadal TSI variation and more research is needed on this topic. It also appears that a modest upward trend over the period since 1850 of 0.11ºC/century is reproduced by the TSI reconstructions. This is a critical point because the rising temperatures from 1850 to 1950 (or 1942) could be due to the secular increase of solar activity observed since the 18th century (Scafetta [8]). Thus, the cause of both the 60-year cycle modulation plus an upward trend since 1850 is likely to be linked to the multidecadal and multisecular oscillatory nature of the solar variation. This is evident over long


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timescales and is apparent in cosmogenic isotopes. For example, Ogurtsov et al. [50] found strong evidence for solar cycles with 60 to 64, 80 to 90, 128, 205, and 1020 year periods in 10Be, 14C, and Wolf number over the past 1000 years. Mazzarella [73] also found a clear link between the 60-year solar cycle and climate oscillations during the last 150 years. 8. DISCUSSION AND CONCLUSION Herein we have argued that the global surface instrumental temperature record, as well as longer term records, contain a quasi 60-year natural cycle. This cycle may be associated with a low frequency modulation of the ocean oscillations such as the ENSO signal. It appears to be present in solar and astronomical records as well, although a perfect match is difficult to obtain because of the uncertainties about the current total solar irradiance proxy models. Better solar irradiance models or alternative solar system direct forcings (Scafetta, [18]) may be required to find a perfect match. Thus, this quasi 60-year cycle observed in the temperature record likely has an astronomical/solar and, therefore, natural origin. The weaker quasi 20-year cycle is not as easily detected in geologic records but it is clearly detected in the global surface instrumental records (Scafetta, [18]). The projected rate of temperature increase due to human influences in our analysis suggests a value for climate sensitivity. Using an estimate of doubling time of greenhouse gases based on historical doubling times for carbon dioxide (Loehle [13]) as a proxy, the anthropogenic warming resulting from doubled forcing would be at least two to three times less than previously thought, that is on average about 1-1.5°C or less from our linear model, which

Fig. (6). Global surface temperature against the phenomenological solar signature obtained with the model by Scafetta [2009] applied to the green and black TSI proxy model curves depicted in Supplemental Information c.


may also suggest the presence of a slight negative feedback to CO2 in the climate system. The models adopted by the IPCC [2] assume that more than 90% of the warming observed since 1850 to 2000 and about 100% of the warming observed since 1970 has been caused by anthropogenic forcing. This information can be deduced from a direct analysis of the figures published there, such as Fig. (9.5a, 9.5b), which claim that natural forcing alone (solar plus volcano) would have induced a cooling since 1970. However, these climate models are not able to reproduce decadal and multi-decadal natural cycles (Scafetta, [18]). We showed that the Hadley global anomaly data from 1850 to 1950 are matched closely by a periodic model with periods 60 and 20 years plus a linear trend. The residuals from this model after 1950 are strictly linear, matching other attribution studies (e.g., Hegerl et al. [23]; Thompson et al. [24]). Based on this result, our full model (natural plus anthropogenic effects after 1942) closely matched the full 160-year record. It had far higher pattern accuracy than any traditional analytical computer general circulation climate model output. We further found no clear evidence for a significant cooling effect due to troposphere aerosols, since our model shows cooling in the 1950s through 1970s without invoking this mechanism, and there are no unexplained residuals corresponding to the period of assumed influence of this factor. Comparing the temperature rise from the 1970s to 2010 (~0.5°C) due to the 60 and 20-year cycles (~0.3°C) with that evident in the model residuals (Fig. 2B, ~0.2 °C), we can see that more than half of the warming since the 1970s is the result of the timing of the upturn of the 20 and 60 year cycles. Our empirical model further predicts a lack of warming since about 2000, in contrast to IPCC model projections of steady linear warming due to anthropogenic forcing alone of about +2.3°C/century from 2000 to 2010 (see IPCC 2007 figure SPM.5), which is a 3.5 time larger rate than the +0.66°C/century warming that we have estimated above. In conclusion, we have shown that the effect of natural oscillations is critical for proper assessment of anthropogenic impacts on the climate system. Since the rapid increase in temperature in the 1980s and 1990s is due partially to natural cycles, then any model-based estimate of climate sensitivity based on elevated greenhouse gases will be too high, a point also made by Scafetta and West [74], Scafetta [8, 18], and Klyashtorin and Lyubushin [44]. The same applies to attribution studies that focus on these decades. Proper consideration of these cycles and of longer natural cycles is also critical for detecting underlying trends. For example, a millenarian or longer period cycles might explain the pattern of the Medieval Warm Period and Little Ice Age (Loehle and Singer [32]) as well as warming since about 1800. It is also worth noting that by starting in a cycle trough in 1900 and ending on a cycle peak in 2000, graphical depictions of warming in IPCC and other reports give an exaggerated depiction of the rate of warming, a point noted by Karlén [75] and Soon et al. [76]. By further failing to mention solar effects and warm bias in the instrumental record, it is implied that almost all the warming of about 0.8°C over this period is human caused, when really no more than 0.4°C is likely to be. Reporting such a lower number would probably cause much less alarm than what was

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recently claimed by Rockström et al. [77]. Moreover, because the 60-year natural cycle will be in its cooling phase for the next 20 years, global temperatures will probably not increase for the next few decades in spite of the important role of human emissions (see Fig. 5A), as predicted by multiple studies (reviewed in Loehle [69]; Scafetta [18]). CONFLICT OF INTEREST No outside funding was used to conduct this work. No financial interests of conflicts of interest are involved. ACKNOWLEDGEMENTS We thank anonymous reviewers for detailed helpful comments. SUPPLEMENTARY MATERIAL This article contains supplementary material and it can be viewed at publisher’s website. REFERENCES [1] [2]

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Received: December 27, 2010

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Accepted: June 4, 2011

© Loehle and Scafetta; Licensee Bentham Open. This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License (http: //creativecommons.org/licenses/bync/3.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.

Criteria for assessing climate change impacts on ecosystems Craig Loehle National Council for Air and Stream Improvement Inc., Washington Street, Naperville, Illinois, USA

Keywords Biodiversity, climate envelope model, extinction risk, General Circulation Model, impact assessment. Correspondence Craig Loehle, National Council for Air and Stream Improvement, Inc., 552 S Washington Street, Suite 224, Naperville, IL 60540 Tel: 630-579-1190; Fax: 630-579-1195; E-mail: [email protected] Received: 05 May 2011; Revised: 09 June 2011; Accepted: 11 June 2011. doi: 10.1002/ece3.7

Abstract There is concern about the potential impacts of climate change on species and ecosystems. To address this concern, a large body of literature has developed in which these impacts are assessed. In this study, criteria for conducting reliable and useful assessments of impacts of future climate are suggested. The major decisions involve: clearly defining an emissions scenario; selecting a climate model; evaluating climate model skill and bias; quantifying General Circulation Model (GCM) between-model variability; selecting an ecosystem model and assessing uncertainty; properly considering transient versus equilibrium responses; including effects of CO2 on plant response; evaluating implications of simplifying assumptions; and considering animal linkage with vegetation. A sample of the literature was surveyed in light of these criteria. Many of the studies used climate simulations that were >10 years old and not representative of best current models. Future effects of elevated CO2 on plant drought resistance and productivity were generally included in growth model studies but not in niche (habitat suitability) studies, causing the latter to forecast greater future adverse impacts. Overly simplified spatial representation was frequent and caused the existence of refugia to be underestimated. Few studies compared multiple climate simulations and ecosystem models (including parametric uncertainty), leading to a false impression of precision and potentially arbitrary results due to high between-model variance. No study assessed climate model retrodictive skill or bias. Overall, most current studies fail to meet all of the proposed criteria. Suggestions for improving assessments are provided.

Introduction The potential future impacts of climate change are of increasing concern. Impacts on ecosystems could potentially affect the capacity of natural systems to produce wood products, crops, livestock, and game. There is also concern about impacts on biodiversity in general and species extinctions in particular. To assess these risks, a voluminous literature has appeared in which ecosystem or species responses to future possible climates are evaluated. These studies often utilize the experimental literature as a basis for longer term extrapolation. Two basic approaches have been used to evaluate long-term natural system response (whereas crops can be evaluated experimentally and are not considered further here): habitat suitability (niche) models and simulations. In habitat suitability models, the climate space (with or without consideration of soils, topography, etc.) where a species or vegetation

type is currently found is characterized by some sort of statistical model, and this model is applied to a future climate scenario (e.g., Aitken et al. 2008). In simulations, a tree, stand, population, or ecosystem growth model is applied to current and scenario cases and the results are compared (e.g., Coops and Waring 2011). While the literature using these approaches is vast, it is not systematic or standardized. Many choices must be made during the analysis that can lead to large and arbitrary differences in study outcomes. For example, Cramer et al. (2001), using six dynamic global vegetation models, gave a range of outcomes for the global carbon sink in 2100 under rising CO2 and climate change of 0.3 to 6.6 Pg C y–1 , an immense range of 22×. Verburg et al. (2011) showed that spatial data errors in mapped attributes can produce larger uncertainties than the process under investigation. Further examples are cited in later sections herein. Furthermore, it will be shown herein that certain key factors have been left out of many analyses,

c 2011 The Author. Published by Blackwell Publishing Ltd. This is an open access article under the terms of the Creative Commons Attribution Non Commercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

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Assessing Climate Change Impacts

potentially affecting their interpretation. Such issues can lead to the observed widespread failures of model forecasting (e.g., Pilkey–Jarvis and Pilkey 2008). The purpose of this study, then, is to propose criteria for developing ecosystem assessments of response to climate change. These criteria involve the choices that must be made at each step of the assessment process. In some cases, it is sufficient to make explicit the limits that a particular choice (e.g., emissions scenario) places on what can be inferred from the study. In others, including or excluding a particular factor or model can create a particular bias that must be taken into account. For example, if a particular climate model predicts too much or too little rainfall in the 20th century base runs compared to actual rainfall, this will bias or distort results of an ecosystem simulation. The presentation of criteria is followed by an assessment of the extent to which the criteria have been addressed in recent literature.

Ecosystem Assessment Criteria The assessment of any ecosystem impact involves a series of steps, especially when long-term effects are being evaluated. Failure to exercise care in this process can lead to underestimation of uncertainty and even to arbitrary results. In order to avoid such negative outcomes, a minimal set of criteria is proposed for some critical steps in this process. More detailed criteria could be developed (e.g., for statistical testing), but this minimal set is sufficient for the present analysis. The criteria for decisions that must be made to put together a complete and valid assessment involve the following: (1) Clearly define emissions scenario. (2) Select climate model(s). (3) Evaluate climate model skill and bias. (4) Quantify General Circulation Model (GCM) betweenmodel variability. (5) Select an ecosystem model and assess uncertainty. (6) Properly consider transient versus equilibrium responses. (7) Include effects of CO2 on plant response. (8) Evaluate implications of simplifying assumptions. (9) Consider animal linkage with vegetation. Each of these criteria is discussed more fully in what follows.

Clearly defining an emissions scenario It is standard practice to evaluate ecosystem impacts in terms of the Intergovernmental Panel on climate change standard scenarios for future emissions of greenhouse gases (Intergovernmental Panel on Climate Change [IPCC] 2007). The first factor to consider is that both the scenarios themselves and the model ensemble warming due to the scenarios differ between the IPCC 2001 (Intergovernmental Panel on Climate Change [IPCC] 2001) and 2007 reports. For example, while

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the A2 2001 scenario is projected to produce 3.8◦ C warming by 2100, the 2007 A2 scenario only projects 3.4◦ C global mean increase by 2100. Thus, studies using the “same” scenario published at earlier or later dates will not be strictly comparable. In addition, model runs from 2001 show wider spread than those from the 2007 report. In particular, earlier studies are likely to show more impact and should be considered to be superseded by more recent ones. Model outputs from earlier scenarios and GCMs may still be available but should no longer be used and will exaggerate impacts if used. A second criterion for utilizing scenarios is to distinguish between worst-case scenario and most likely cases. If the worst-case scenario is used in the assessment, it should not be stated that the simulated impact is “what will” happen, but rather is a worst case. If evaluation of a likely scenario is desired, the appropriate case should be used.

Selecting a climate model Early (before 1995 or even up to 2000+) GCMs either did not simulate precipitation or were known to do so poorly (Corte–Real et al. 1995). This includes most of the models used in the 2001 IPCC report. For evaluating ecosystem impacts, this limitation cannot be addressed by using a constant precipitation regime. If precipitation is held constant and temperature is increased, one obviously would predict negative effects on plants. Theory predicts, in contrast, that a warmer climate will be accompanied by increased precipitation (Wentz et al. 2007; Schliep et al. 2010; Stephens et al. 2010). Whether increased evaporative demand will be balanced by increased precipitation is an open question and one on which different models do not necessarily agree. The future predicted water balance will also vary regionally, from wetter to drier to neutral. Thus, it is critical that older GCM models/model runs no longer be used for any ecosystem studies that involve water balance. Further, in literature summaries, older studies should not be given equal weight to those using more recent GCM results.

Evaluating climate model skill and bias Output from a climate model is often used to evaluate ecosystem response, perhaps with a regional downscaling first. These outputs are virtually always taken at face value for conducting impact studies. Any particular model, however, may have known skill and bias issues with respect to regional climates. Skill can be evaluated by matching the pattern in time or distribution of temperatures or rainfall between the model and historical data. For example, a GCM might produce skillful annual precipitation amounts, but the seasonal distribution could be very different from actual. The seasonal amounts could be critical to properly simulating plant growth. Similarly, a GCM could consistently predict temperatures too warm for a region, even though matching historical

c 2011 The Author. Published by Blackwell Publishing Ltd.

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trends. This could lead to modeled impacts that are not realistic. In IPCC assessments, in which global trends over 100 years are being evaluated, these skill and bias issues may not affect the calculation of trends, but at the regional/local scale for simulating ecosystem response, they cannot be ignored. Anagnostopoulos et al. (2010) compared six climate models at multiple scales to weather data for the contiguous United States. Model results were all warmer than the observed mean annual temperature and minimum monthly temperature by up to 4◦ C. This bias creates an obvious problem for any bioclimatic (niche) or simulation model used to forecast future distributions because absolute temperatures, not just trends, are critical to biological processes. This is particularly true when models are developed using actual (not model) climate data, which do not share the model bias. Likewise, at the continental scale, they found modeled precipitation to be 36% above the true value, which has implications for any biological model. Schliep et al. (2010) and Stephens et al. (2010) noted multiple failings in the ability of GCMs to model precipitation, including both bias and lack of skill. Consider a climate model that produces output at the dry end of what occurs in a particular region. A model of forest growth is run under constant climate, but because of the climate model’s temperature bias, the simulated forest is near the point at which drought stress would cause dieback. Under almost any additional warming, this simulated forest using this climate model would show dieback. This bias can be addressed by comparing the GCM output to local weather data and adjusting the model bias (as Ines and Hansen 2006) before simulating forest growth (for both control and impact scenarios). If it is determined that skill is lacking (e.g., rainfall frequency distribution is wildly wrong or seasonal temperatures are not proportional) then another model should be used. In any case, climate model adequacy for the intended purpose should be evaluated rather than treating model output as if it were “data.”


predicted habitat between two climate models for an emissions scenario were greater than the differences between scenarios using the same climate model. Given the large number of climate models and their difficulties modeling regional climate (noted above), it is apparent that vastly different ecological impacts could be predicted depending on the model used and the geographic region of interest, thereby violating criteria of rigor and reproducibility. One way around the problem is to use multiple models to sample the range of possible projections. This is especially important because at the regional scale some models may project wetter and some drier conditions. While this solution requires more work to perform an analysis, a valid result depends on some estimate of uncertainty.

Selecting an ecosystem model and assessing uncertainty Just as with climate models, ecosystem models can differ in their skill and bias as well as in their suitability for a particular task, and may produce widely varying results between models (Cramer et al. 2001). The reasons for choosing a particular model for an assessment need to be clearly stated. Process models can have considerable parametric uncertainty (e.g., Larocque et al. 2011) that can carry through the analysis to reduce outcome certainty. Ignoring these issues can make it look like model output is without error, whereas it is standard practice to report confidence intervals on experimental results. Niche (suitability) models likewise are not guaranteed to ´ 2004; Araujo ´ and Guisan be accurate (Segurado and Araujo 2006). Classification accuracy of niche models does not tell the full story. Beale et al. (2008) and Pearson and Dawson (2003) provide a more complete discussion of the inherent uncertainty in these models including issues of variable covariance and spurious correlation. There is unfortunately no modeling method that has unambiguously been shown to be universally superior. It is critical to evaluate uncertainty due to these issues.

Quantifying GCM between-model variability GCMs exhibit considerable variation in outputs (Furrer et al. 2007), especially at regional scales (e.g., Rowell 2006). For example, Woollings (2010) found that simulations for Europe differed between models (and from actual weather) for the jet stream location, zonal air flow, blocking highs, and other key weather patterns. It is important to note that models do not differ only in trends or stochastically, but also in terms of seasonality and spatial patterns of weather. There may not be any sense in which these differences “average out” between models. Few studies have compared ecosystem impacts using more than one climate model. In a study of animal habitat suitability in Spain, Real et al. (2010) found that the differences in


Properly considering transient versus equilibrium responses A large part of the ecosystem response literature consists of the analysis of habitat suitability (or geographic range) maps. Data on climate and other variables are used to predict presence of a species, forest type, or ecosystem using regression analysis or other tools. This model is then applied to futuremodeled climates. A large shift in the range is usually considered to be an indicator of extinction risk because species, especially plants, are not able to migrate rapidly (e.g., Aitken et al. 2008; Morin et al. 2008). Implicit in these conclusions is the equating of “suitable ´ habitat” with the habitat necessary for survival (e.g., Araujo

3


and Guisan 2006). However, the presence of a species means that it is competitive in the area where it occurs, not just that it can survive there. Small differences in competitive ability will eventually work themselves out and show up in distribution data, but this process may take a very long time, especially for long-lived organisms such as trees. Conditions outside the realized niche (i.e., where the species currently occurs) cannot be assumed to be lethal without proof. Projected climate changes are small compared to even daily temperature variation and, for most species, are not near lethal limits (e.g., Wertin et al. 2010). This general point was discussed in Loehle and LeBlanc (1996) but needs repeating because the confusion between transient and equilibrium response continues. Multiple studies suggest that actual vegetation responses to even large climate shifts are slow. Cole (1985, 2009) documented a several thousand year transition in the Grand Canyon following past warming episodes. Cwynar and Spear (1991) showed that boreal forest advance in response to past warming took up to 1000 years, with a faster retreat due to cooling (which damages trees and prevents regeneration), as also shown by Tinner et al. (2007) for boreal dieback during the Little Ice Age. Williams et al. (2011) documented a slow late Quaternary boreal forest expansion across the Northern Hemisphere. In the boreal forest, where 20th century warming would suggest rapid geographic displacement, the response has been very slow (Masek 2001; Payette 2007). In no case has any study documented a “dead zone” in response to a past climate shift, as is implied by static habitat suitability models. Simulation models also suggest that such transitions should be gradual (Noble 1993; Loehle 2000, 2003). Application of these criteria involves the careful delineation of implications from a study. It is not necessary that dynamic simulations be used. Rather, it is critical that information on transient response (relaxation times) can be used to qualify conclusions. If the study concerns mobile species, equilibrium might be quickly achieved, but if it concerns trees it could take hundreds to thousands of years. Nonoverlap of current and hypothetical geographic ranges does not mean extinction unless climate lethality can be proven by other means.

Including effects of CO2 on plant response CO2 enrichment directly increases growth and water-use efficiency (WUE) in plants (Medlyn et al. 2001; Leuzinger and Körner 2007; Loehle 2007; Kohler et al. 2010), with effects differing by taxa and CO2 level. Increased WUE is particularly important when water is limiting, and has been shown to mitigate considerable drought stress (e.g., de Graff et al. 2006; Wertin et al. 2010). WUE is not considered here in terms of hydrology but only with respect to drought response. Freeair CO2 exchange (FACE) and open top chambers have both

4

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shown a positive response of plant growth to CO2 enrichment (Curtis and Wang 1998; de Graaf et al. 2006). For plant growth or habitat suitability models, failure to include CO2 effects and WUE increases with time over the next 100 years will lead to significant overestimates of negative impacts of elevated temperature, reduced moisture, or both together (Cramer et al. 2001; Pan et al. 2009; Friend 2010). Mechanistic models that incorporate CO2 effects are generally based on experimental results such as FACE experiments, in some cases with medium-sized trees over many years. Keenan et al. (2011) simulated forest growth with and without CO2 effects. Without including CO2 effects in the simulation, all three species showed decreased net primary productivity (NPP) over time under the warming scenario. However, when CO2 effects were included, the species exhibited increased growth until about 2070 followed by a slight decline, ending at 2100 with slightly higher NPP than in 2000. How CO2 effects are addressed can make the difference between positive and negative growth responses under many scenarios. It is also the case, of course, that no CO2 experiments have gone as far as 100 years, covered large spatial extents, or allowed for species shifts, so models incorporating CO2 effects may give unrealistic or exaggerated responses at some time/space scales (Leuzinger et al. 2011).

Evaluating implications of simplifying assumptions In order to conduct a geographic analysis of climate effects, it is often necessary to make simplifying assumptions due to data resolution. We may, for example, have GCM output at 1◦ × 1◦ resolution. In the real world, however, fine-scale topographic effects moderate regional climate in complex terrain. An analysis based on coarse-scale data might suggest that no suitable habitat remains under some scenario, when in fact topography might provide for refugia (Scherrer and Körner 2010; Dobrowski 2011; Godfree et al. 2011). Other simplifications might be made, such as using a single very dry soil type for the simulation (e.g., Coops and Waring 2011). The criterion here is that such simplifications, while perhaps necessary because of data or computational limitations, have implications that qualify the results and should be discussed. In addition, geographic data can have errors resulting from vegetation classification, measurement error and bias, aggregation error, and other sources, leading to effects possibly larger than those resulting from the scenarios being evaluated (Verburg et al. 2011).

Considering animal linkage with vegetation When suitability/range map-type models are developed for plants, there are good reasons to suppose that climate directly determines plant distribution (as modified by competition and fire, or course). It is equally easy to develop models for



C. Loehle

animals’ range, but these models might actually be proxies for the vegetation on which the animals depend as much as for climate per se. That is, spurious correlation is a real danger in the absence of experimental data. For example, ruminants depend on grasslands and waterfowl depend on marshes and lakes. If an animal depends on certain vegetation that exhibits a long lag in responding to climate, the animal may likewise persist in its current range unless its physiological tolerance is actually exceeded. If the niche model uses climate variables, the projection of animal response could be unrealistic. Thus, it should be recognized that suitability models are correlational rather than fundamental, and investigators should carefully consider model output in this light.

Literature Survey A literature survey was conducted to evaluate compliance with the assessment criteria suggested here. All issues of Global Change Biology in 2009 and 2010 and the first four issues in 2011 were assessed. All terrestrial studies discussing future impacts on species or ecosystems were considered (not carbon or fire studies). Experimental and field studies were outside the scope of the review. A total of 20 papers were evaluated (Table 1), with one study evaluated under two categories. In the author’s experience, these 20 papers are similar to others in the field and serve to illustrate the points made.

Growth models Nine papers (seven of vegetation) with some sort of dynamic or growth model utilized a variety of approaches and often

met most of the criteria proposed for impact assessments. Most used the most recent IPCC AR4 climate simulations. Keenan et al. (2011) did underestimate outcome variability by only using one climate simulation and one forest growth model, but is the only study that directly compared niche and growth model results (using multiple niche models but a single vegetation model). The inclusion of CO2 effects led to an opposite conclusion on impacts (positive) compared to leaving them out (negative). Doherty et al. (2010) evaluated effects of future climate on NPP of East Africa. They used output from nine GCM models to capture uncertainty but only a single vegetation model that did simulate CO2 enrichment effects. All cases projected increased NPP in the study area. Poulter et al. (2010) simulated response of the Amazon forest to CO2 and eight different GCMs as well as vegetation model parameter uncertainty. They found that between-climatemodel differences dominated outcome spread compared to CO2 or vegetation model parameter uncertainty, with future precipitation uncertainty the greatest unknown determinant of outcomes. Chen et al. (2010) evaluated response of Douglas-fir to projected climate change using a tree ring index model of growth response. Variability was captured by using output from five climate models. The inability of this approach to include effects of rising CO2 is problematic because the main impacts of climate change in their analysis were manifested via drought stress, which rising CO2 and thus increasing WUE would help ameliorate. Xu et al. (2009) simulated forest growth in northern Minnesota using a single process model. CO2 was included in the model. Future climate data (27

Table 1. Percent compliance with criteria for literature survey cases (percentages calculated only for criteria relevant to the studies in question). NA reflects that criterion not relevant to that model type. Criteriaa

Dynamic models Plant Animal Niche models Plant Animal

n

1

2

3

4

5

6

7

8

9

7 2

1.0 0

0.86 0.5

0 0

0.57 0.5

0.14 1.0

1.0 1.0

0.71 NA

1.0 0

NA 0

7 5

0.71 0.8

0.43 0.6

0 0

0.43 0.4

0.43 0.4

0 0

0 0

0.57 0

NA 0.6

NA = not applicable a criteria areas follows: (1) Clearly define emissions scenario. (2) Select climate model(s). (3) Evaluate climate model skill and bias. (4) Quantify GCM between-model variability. (5) Select an ecosystem model and assess uncertainty. (6) Properly consider transient versus equilibrium responses. (7) Include effects of CO2 on plant response. (8) Evaluate implications of simplifying assumptions. (9) Consider animal linkage with vegetation.


5


profiles) were mostly from older GCMs used in the third IPCC assessment in 2001. The large number of climate runs allowed the large uncertainty due to forecasting to be quantified. Morin et al. (2009) used a process-based statistical model for leaf unfolding for 22 North American tree species as a function of climate. A single GCM run from 2001 was used for the A2 and B2 scenarios only. The effect of CO2 on phenology was not assessed. Spatial resolution was probably not an issue. With a single older GCM run, there is no way to evaluate the representativeness of their forecasts. Scheiter and Higgens (2009) used a mechanistic model of grassland and forest to evaluate response of the vegetation of Africa to climate change. The model included CO2 effects that were believed to account for increased forest area and biomass as well as the increase in total vegetated land (at the expense of desert) by 2100. Spatial resolution was good at the scale of 1 ha. The GCM used was a 2007 model run, but the lack of intermodel comparison makes it difficult to evaluate results. Two animal process models were found. Gilg et al. (2009) explored effects of future climate on Arctic predator–prey systems. The climate scenarios were qualitative and hence difficult to evaluate. Snäll et al. (2009) used seven up-to-date GCM models and a process model for plague in prairie dogs. By sampling from the plague model parameter uncertainty space as well as the multiple GCMs they were able to do an exemplary job quantifying uncertainty, although they did not assess GCM bias or skill. Overall, process-based studies did a good job assessing uncertainty due to between climate model variation and within ecosystem model parametric uncertainty, although only Snäll et al. (2009) and Poulter et al. (2010) evaluated both. Most used current climate models but none evaluated GCM skill and bias issues.

Niche models All seven studies using niche models for plant distributions (Bradley 2009; Bradley et al. 2009; Randin et al. 2009; Feeley and Silman 2010; Dirnböck and Rabitsch 2011; Dlamini 2011; Keenan et al. 2011) failed to consider CO2 effects on future growth. Because much of the impact of future climate on plants results from net water deficiency, the lack of consideration of CO2 effects calls results into question, especially in light of Keenan et al.’s (2011) results discussed above. Use of future climate scenarios was mixed. Keenan et al. (2011) used IPCC AR4 climate model results, but Dlamini (2011) used a result from 2000, which is rather old. Dirnböck and Rabitsch (2011) and Feeley and Silman (2010) used simple increases in temperature, including an extreme 8◦ C rise in the latter case, with no increase in precipitation. Failure to consider future precipitation changes generally will make impacts more negative for plants. Uncertainty due to niche model differences was considered by Keenan et al. (2011), and that due to

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between-climate model differences by Dlamini (2011). The equilibrium assumption (equating niche model changes with immediate population changes) was universal in these studies, with only a mention of possible lags by Dirnböck and Rabitsch (2011), although possible lags were considered to be in terms of mere decades. Two studies of invasive plants (Bradley 2009; Bradley et al. 2009) using niche models used 10 AR4 GCM runs and were thus able to quantify model-based uncertainty, but did not assess model skill or bias and did not clearly cite the model runs used. Because they only evaluated potential habitat for invasion, there was less problem with the equilibrium assumption, but they did assume that future unsuitable habitat for the invasive species would immediately become a conservation opportunity. Randin et al. (2009) compared scenarios at 50 km × 50 km versus 25 m × 25 m plot scales. Because they interpolated climate to account for topography at the smaller plot size, the local-scale models showed persistence of up to 100% of species that the European-scale model predicted would lose their entire habitat. The study was limited by the use of (apparently) an older single (approximately 2000) GCM run. The net effect in all these models of excluding the beneficial effects of elevated CO2 and assuming rapid vegetation changes due to changes in available suitable habitat is to greatly amplify likely negative effects or even to convert positive effects into negative ones, as in Keenan et al. (2011). Likewise, niche models at too coarse a resolution will miss refugia from climate change. Five niche models were used to assess animal distributions (Jarema et al. 2009; Carroll et al. 2010; Carvalho et al. 2010; Rebelo et al. 2010; Habel et al. 2011). These studies used climate projections from 2004 or 2005 except for Carvalho et al. (2010) for which the date of the climate model could not be determined and Jarema et al. (2009), which used a 2001 GCM run. Carvalho et al. (2010) used more than one niche model (nine) and GCM (three) and Jarema et al. (2009) used two GCM models and multiple niche models. Carroll et al. (2010) developed animal niche models that included vegetation and held vegetation constant based on consideration of lags that were likely to occur in forests of the Pacific Northwest United States. Jarema et al. (2009) found niche models based on habitat to be almost as good as those using climate variables alone. All other studies used niche models dominated by climate variables and thus ignored the possibility that animals may be found in their current habitat based on the vegetation, which would probably respond to climate change in a lagged fashion. They all further assumed equilibrium response, whereas animal distributions are likely to be heavily influenced by competition and predation effects that would take time to come to equilibrium in a new climate. Finally, a fairly extreme scenario of 6◦ C warming was used by Rebelo et al. (2010). Simplifying assumptions that may have affected results include use of 35 km2 grid cells in Dirnböck and Rabitsch (2011), only a simple temperature


C. Loehle

gradient (no heterogeneity or precipitation) in Feeley and Silman (2010), the coarse spatial scale used in Carvalho et al. (2010) that would not allow for refugia, and the assumption in Rebelo et al. (2010) that bats are dispersal-limited. Overall, uncertainty was underestimated and effects were probably exaggerated in this set of studies.

Discussion The criteria suggested in this study are intended to guide decisions in assessments of future climate change impacts and to help prevent results from being arbitrary or showing false precision due to lack of uncertainty information. The literature survey revealed that recent studies have not considered some of the proposed criteria (Table 1). Violation of any of the criteria can be serious, but some are more easily remedied than others. The criteria are next considered in turn, with suggestions for improved assessments. The examined papers generally referred back to IPCC AR4 emissions scenarios (IPCC 2007). Usually, either the A2 scenario was used or a range of scenarios was evaluated. This standard set of scenarios helps with interstudy comparisons. Some studies used arbitrary temperature increases rather than model outputs, which has the added problem that precipitation was held constant or ignored. If simple temperature increases are evaluated, they should at least be more clearly related to model scenarios. It is difficult to justify leaving precipitation changes out of an assessment and not difficult to add this factor. Recent generation climate simulations were not necessarily used, although the growth model studies mostly used up-to-date results. Four of seven plant niche models used out-of-date climate models. There is really no good reason to use old climate simulations. No study took into account the skill or bias of the climate models. This is problematic, especially because many used models for current ecosystems calibrated with actual weather data and compared them to future simulated climates in which a bias (offset) could exist. At least a qualitative assessment of climate model outputs compared to the study area seems necessary before doing an assessment. Of the sources of uncertainty (climate model, and niche model or growth model), only three studies (Jarema et al. 2009; Carvalho et al. 2010; Poulter et al. 2010) considered both and many studies included only a single model and GCM dataset. It is not impossible to avoid this problem by explicitly using the multiple climate model outputs, which are becoming increasingly available for regional scales. Likewise, ensemble means of climate data might be available and ensemble means or overlap ´ and New 2007) are not difficult to for niche models (Araujo develop, since the statistical tools are widely available. Studies using niche models mostly failed to properly consider lags in vegetation response to climate change. They assumed that habitat defined as “unsuitable” by the niche model



was immediately uninhabitable by the species or vegetation without presenting evidence that the altered climate would be actually lethal. Instead, competitive displacement, an inherently slow process that may take centuries (Noble 1993; Loehle 2000, 2003), could eventually result in the changes suggested by niche models. While niche models are inherently equilibrium based, it is possible to perform some assessment ´ and Pearson 2005). of nonequilibrium response (e.g., Araujo In addition, data do exist on drought and heat tolerance of many species, and such data should be consulted before making claims that species or ecosystems will perish/vanish over the next few decades due to a few degrees warming. If the temperature rise is within the thermal tolerance of a species, then range shifts, not extinctions, are going to result and may take centuries. Consulting the literature to evaluate the likely time dynamics of the output of niche models is not a burdensome requirement. Most studies using growth models included beneficial effects of elevated CO2 on plant growth. It is noteworthy that these models forecast much less impact or even positive effects compared to niche models, which are inherently unable to account for future elevated CO2 effects. While the exact effects of elevated CO2 over the long term are not known (e.g., Leuzinger et al. 2011), the experimental literature seems to support a growth-enhancement conclusion and models have widely adopted this result. It would seem that dynamic (mechanistic) models of plant response to climate change should be preferred, though niche models are much more widely published, perhaps due to simplicity and cost. Cost alone does not justify a method that leads to the wrong answer. At the very least, the result of any niche analysis needs to be qualified by discussion of how CO2 effects would alter the result. The primary simplifying assumption that impacted results in a number of studies was the reduction of spatial heterogeneity considered, either due to grid scale or to elevational gradient representation (e.g., representing it as a smooth curve). Spatial heterogeneity can provide refugia, and failure to consider this effect will tend to exaggerate impacts, as clearly shown by Randin et al. (2009). The limiting factors for performing more spatially resolved analyses are computer time and input data. The former can be overcome by distributed computing or weekend runs. The latter is at least mitigated by using the highest resolution data available rather than aggregated data. The conceptual and analysis steps are identical in either case. Finally, in animal impact studies, a pervasive trend was to model animal response solely as a function of climate. If animals select habitat based on vegetation and the response of vegetation to changing climate is lagged, the response of animals will probably also be lagged. The assumption that animals are limited by climate as defined by a niche model is virtually unverified.

7


When study results can be completely altered by including or excluding a factor such as CO2 or spatial heterogeneity and when between-model forecasts can be extremely variable, it is essential that these and other known factors be recognized and addressed in any analysis of climate change effects. The majority of surveyed studies might have yielded very different results if the criteria suggested here had been applied. More attention to criteria such as those proposed would lead to much more reliable and useful impact assessments. It has been shown above that most of these problems are easily fixed, require a modest effort (e.g., using niche model ensembles), or can be dealt with in the discussion of results.

Acknowledgments No outside funding was received for this study. I thank A. Lucier, D. Sleep, J. Verschuyl, and B. Wigley for helpful comments on the manuscript. References Aitken, S. N., S. Yeaman, J. A. Holliday, T. Wang, and S. Curtis-McLane. 2008. Adaptation, migration or extirpation: climate change outcomes for tree populations. Evol. Appl. 1:95–111. ´ M. B., and A. Guisan. 2006. Five (or so) challenges for Araujo, species distribution modelling. J. Biogeogr. 33:1677–1688. ´ M. B., and M. New. 2007. Ensemble forecasting of species Araujo, distributions. Trends Ecol. Evol. 22:42–47. ´ M. B., and R. G. Pearson. 2005. Equilibrium of species’ Araujo, distributions with climate. Ecography 28:693–695. Anagnostopoulos, G. G., D. Koutsoyiannis, A. Christofides, A. Efstradiadis, and N. Mamassis. 2010. A comparison of local and aggregated climate model outputs with observed data. Hydrolog. Sci. J. 55:1094–1110. Beale, C. M., J. J. Lennon, and A. Gimona. 2008. Opening the climate envelope reveals no macroscale associations with climate in European birds. Proc. Nat. Acad. Sci. 105:14908–14912. Bradley, B. A. 2009. Regional analysis of the impacts of climate change on cheatgrass invasion shows potential risk and opportunity. Glob. Change Biol. 15:196–208. Bradley, B. A., M. Oppenheimer, and D. S. Wilcove. 2009. Climate change and plant invasions: restoration opportunities ahead? Glob. Change Biol. 15:1511–1521. Carroll, C., J. R. Dunk, and A. Moilanen. 2010. Optimizing resiliency of reserve networks to climate change: multispecies conservation planning in the Pacific Northwest, USA. Glob. Change Biol. 16:891–904. Carvalho, S. B., J. C. Brito, E. J. Crespo, and H. P. Possingham. 2010. From climate change predictions to actions – conserving vulnerable animal groups in hotspots at a regional scale. Glob. Change Biol. 16:3257–3270. Chen, P.-Y., C. Welsh, and A. Hamann. 2010. Geographic variation in growth response of Douglas-fir to interannual

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TREND ANALYSIS OF SATELLITE GLOBAL TEMPERATURE DATA by Craig Loehle (USA)

Reprinted from

ENERGY & ENVIRONMENT VOLUME 20 No. 7 2009

MULTI-SCIENCE PUBLISHING CO. LTD. 5 Wates Way, Brentwood, Essex CM15 9TB, United Kingdom

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TREND ANALYSIS OF SATELLITE GLOBAL TEMPERATURE DATA Craig Loehle National Council for Air and Stream Improvement, Inc. 552 S Washington Street, Suite 224 Naperville, Illinois 60540 phone: 630-579-1190 fax: 630-579-1195 [email protected]

ABSTRACT Global satellite data is analyzed for temperature trends for the period January 1979 through June 2009. Beginning and ending segments show a cooling trend, while the middle segment evinces a warming trend. The past 12 to 13 years show cooling using both satellite data sets, with lower confidence limits that do not exclude a negative trend until 16 years. It is shown that several published studies have predicted cooling in this time frame. One of these models is extrapolated from its 2000 calibration end date and shows a good match to the satellite data, with a projection of continued cooling for several more decades.

INTRODUCTION Temperature trends provide critical evidence for evaluating claims regarding anthropogenic climate change. On the one hand, models project continued warming (e.g., Hansen et al., 2006). On the other, it has been argued that Earth’s weather should be expected to exhibit long-term persistence (LTP) at some scale (Cohn and Lins, 2005; Easterling and Wehner, 2009; Wood, 2008). LTP could at any given time give a false impression of the strength of anthropogenic effects by adding a warming trend to an existing anthropogenic signal, or it could act to counter such signal for some period of years or decades. Thus it is critical to examine climate history data at multiple scales to evaluate these effects. Typical trend studies, however, have not evaluated the most recent decades per se. For example, periods evaluated include sea surface temperatures over 1960-1990 (Casey and Cornillon, 2001), lower troposphere RSS data over 1979-2001 (Fu et al., 2004), the global surface record for 1950-2004, 1979-2004, 1950-1980 (Vose et al., 2005), 1988-2005 (Hansen et al., 2006), and 1977-2001 (Jones and Moberg, 2003), the NCEP reanalysis Northern Hemisphere surface record for 1960-2000 (Lucarini and Russell, 2002), 1969-2000 for Southern Hemisphere data (Thompson and Solomon, 2002), and the satellite and balloon data for 1979-2004 (Christy et al., 2007). The most recent Intergovernmental Panel on Climate Change report (IPCC, 2007) shows 100 and 140 year trends. In all cases, the

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period of rapid warming in the late 1970s through 1990s was included in these analyses. All of these studies looked only at long-term trends. Given that satellite data are now available for more than 30 years and that recent years do not show a visible upward trend, it seems appropriate to re-examine temperature trends. The purpose here is not to obtain the 30 year trend, which has been done previously, but rather to parse the data to evaluate evidence for LTP and recent trends. In spite of the importance of the satellite data for assessing trends, no analysis of trends for the most recent decades has been published using these data. METHODS The purpose of the analysis is to examine the data for linear trends. Looking backward from the most recent dates, the question is what is the temperature trend over recent years? If it is negative (cooling), how long can a cooling trend be said to have existed? Starting at the beginning of the record in 1979 and looking forward, how long a record is needed before a warming trend is detectable? Starting with intervals in the middle of the record, how does the trend in this middle interval compare to the starting and ending periods? Linear trends are the simplest way to assess climate change, and are used in the IPCC reports and most of the trend studies cited in the introduction, among many others. Linear trends also have the advantage that confidence intervals are well defined, which aids in interpretation. Calculating such linear trends overcomes issues due to subjective interpretation of noisy data and the arbitrariness of various methods of smoothing the data, especially at the end points (see Soon et al., 2004). By showing trends of data segments of multiple lengths, issues of the studied interval being unduly influential are avoided. Longer-term trends (or red noise) are graphically uncovered by the analysis, if they exist, and are not being estimated per se. Instead the questions are strictly empirical: how much has it warmed/cooled over various lengths of time, according to satellite data? University of Alabama at Huntsville (UAH) and Remote Sensing Systems (RSS) Microwave Sounding Unit (MSU) data were obtained on July 20, 2009. UAH data were downloaded from http://vortex.nsstc.uah.edu/data/msu/t2lt/uahncdc.lt (Fig. 1a). RSS (http://www.ssmi.com/msu/msu_data_description.html) data were downloaded from ftp://ftp.ssmi.com/msu/monthly_time_series/rss_monthly_msu_amsu_channel_tlt_ano malies_land_and_ocean_v03_2.txt (Fig. 1b). Data from January 1979 through June 2009 (366 months) were available from both series. The data were not smoothed. Series of different length were considered, with 60 months being the shortest. Data were considered for intervals that began in January 1979, for intervals that ended in March 2009, and for intervals starting at the record midpoint. Linear regression was performed using the Regress function in Mathematica (www.Wolfram.com). Slopes were computed on a per decade basis. Adjusted slope confidence intervals were calculated to account for temporal autocorrelation. The single month-to-month correlation of regression residuals, r, was calculated as n −1

( )

r1 = ∑ ( et et +1 ) / ( n − k − 1) / s 2 t =1

(1)

where e are the regression residuals at t, n is the sample size, k is the number of

Trend Analysis of RSS and UAH MSU Global Temperature Data

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parameters (1 here), and s2 is the residuals’ variance. The effective sample size ne is (Bartlett 1935; Quenouille 1952; Santer et al. 2008)

ne = n

(1 − r1 ) (1 + r1 )

(2)

where ne 40 year) trends of Northern-Hemisphere-gridded surface temperature: A note of caution. Geophysical Research Letters 31, L03209, doi:10.1029/2003GL019141. Swanson, K.L. and A.A. Tsonis. 2009. Has the climate recently shifted? Geophysical Research Letters 36, L06711, doi:10.1029/2008GL037022. Thompson, D.W.J. and S. Solomon. Interpretation of recent Southern Hemisphere climate change. Science 296:895-899. Tsonis, A.A., K. Swanson, and S. Kravtsov. 2007. A new dynamical mechanism for major climate shifts. Geophysical Research Letters 34, L13705, doi:10.1029/2007GL030288. Vose, R.S., D.R. Easterling, and B. Gleason. 2005. Maximum and minimum temperature trends for the globe: An update through 2004. Geophysical Research Letters 32, L23822, doi:10.1029/2005GL024379. Wang, G., K.L. Swanson, and A.A. Tsonis. 2009. The pacemaker of major climate shifts. Geophysical Research Letters 36, L07708, doi:10.1029/2008GL036874. Wood, R. 2008. Natural ups and downs. Nature 453:43-45. Yousef, S.M. 2006. 80-120 yr Long-term solar induced effects on the earth, past and predictions. Physics and Chemistry of the Earth 31:113-122; doi:10.1016/j.poe.2005.04.004. Zhen-Shan, L. and S. Xian. 2007. Multi-scale analysis of global temperature changes and trend of a drop in temperature in the next 20 years. Meteorology and Atmospheric Physics 95:115-121.

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COOLING OF THE GLOBAL OCEAN SINCE 2003 Craig Loehle, Ph.D. National Council for Air and Stream Improvement, Inc. (NCASI) 552 S Washington Street, Suite 224 Naperville IL 60540 tel: 630-579-1190; fax: 630-579-1195 [email protected]

ABSTRACT Ocean heat content data from 2003 to 2008 (4.5 years) were evaluated for trend. A trend plus periodic (annual cycle) model fit with R2 = 0.85. The linear component of the model showed a trend of -0.35 (±0.2) x 1022 Joules per year. The result is consistent with other data showing a lack of warming over the past few years. Key Words: climate change, ocean heat content, trend analysis

1.0 INTRODUCTION There is great interest in detecting rates of temperature change in the earth system. It has been suggested (e.g., Pielke 2003) that changes in ocean heat content should be particularly informative. A recent study (Lyman et al. 2006) claimed to find rapid cooling of the ocean between 2003 and 2005, but it was later determined that data from certain instruments caused a substantial cool bias in the result (Willis et al. 2007, 2008a; Wijffels et al. 2008). A corrected and longer dataset has now become available to redo this analysis. 2.0 METHODS The study is based on ocean heat content anomaly (OHCA) data compiled by Willis et al. (2008b). This monthly dataset (Fig. 1) uses only data from the Argo array of profiling floats. Heat content is evaluated down to 900 m depth. The objective is to estimate the linear trend in heat content. However, there is an obvious one year periodicity in the data (Fig. 1a) as noted by Willis et al. (2008b). Proper assessment of trend needs to take this into account, especially when the data are over a 4.5-year interval. Therefore, a model was fit with slope, intercept, and sinusoidal (1-year fixed period) terms using nonlinear least-squares estimation. The model allowed the cycle amplitude to change linearly with time. Before fitting, the data were minimally smoothed with a 1-2-1 filter (Fig. 1b).

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Figure 1. a) Ocean heat content smoothed with a 1-2-1 filter b) Heat content smoothed with 1-2-1 filter and overlaid with best-fit linear plus sinusoidal (seasonal) model (R2 = 0.85) c) Heat content smoothed with 1-2-1 filter and overlaid with linear trend portion of best-fit model (slope = -0.35 x 1022 J/yr)

3.0 RESULTS The model, fit to the smoothed data, gave an excellent fit (r = 0.922, R2 = 0.85) and showed clearly that there is an annual periodicity in the data (Fig. 1b), probably due to the north-south asymmetry in ocean area and the effect of orbital variations over the

Cooling of the Global Ocean Since 2003

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year. The peak-to-trough amplitude of the model is 6.30 x 1022 Joules (J) at the beginning of the period and declines to 3.88 x 1022 J at the end, showing a damping of the cycle over the 4.5-year period. The slope of the linear component of the model (Fig. 1c) is -0.35 x 1022 J/yr, which is 6.9% of the average annual cycle amplitude. The 95% confidence intervals on the trend are from -0.148 x 1022 to -0.550 x 1022 J/yr. This result clearly excludes warming as a possible interpretation of this data. Examination of residuals from the model fit shows no evidence of nonlinearities, indicating a constant linear cooling trend. Over the 4.5-year period of the data, this equates to a total non-seasonal loss of 1.572 x 1022 (0.668 to 2.48 x 1022) J of heat, which is 31% of the average annual cycle amplitude over the interval. 4.0 DISCUSSION It has previously been estimated by Willis et al. (2004) that from 1993 to 2003 the upper ocean gained 8.1 (±1.4) x 1022 J of heat. This study estimates a loss since then of from 0.668 to 2.48 x 1022 J, or 19.4% (up to 31%) of the gain of the prior decade. Ishii and Kimoto (In Press) also show a bias-corrected cooling from 2003 to 2006. On an annual basis, this is a cooling of 0.35 x 1022 J compared to 0.81 x 1022 J warming for 1993 to 2003 (Willis et al. 2004) and slightly less for the same period to 700 m in Ishii and Kimoto (in press). Dominguez et al. (2008) show a 700 m depth annual warming from 1961 to 2003 of 0.38 x 1022 J. Thus the estimate of cooling in the present study is not out of line with past results. It is also consistent with satellite and surface instrumental records that do not show a warming trend over recent years. Another bias-corrected estimate (Gouretski and Koltermann 2007) is based on depth profiles too different to make a comparison. By comparison, Willis et al. (2008a) do not find any significant trend (slight negative trend) for 2003 to 2006, but had a shorter record and performed their trend analysis using simple annual means. Heat loss from the ocean has been estimated to also have occurred in the 1980s (Ishii and Kimoto, In Press; Gouretski and Koltermann 2007; Levitus et al. 2001). The data also indicate an interesting damping with time of the annual fluctuations in heat gain and loss (Fig. 1b). While the current study takes advantage of a globally consistent data source, a 4.5-year period of ocean cooling is not unexpected in terms of natural fluctuations. The problem of instrumental drift and bias is quite complicated, however, (Domingues et al. 2008; Gouretski and Koltermann 2007; Wijffels et al. 2008; Willis et al. 2004, 2008a) and it remains possible that the result of the present analysis is an artifact. ACKNOWLEDGMENTS: Thanks to J. Willis for providing ARGOS data.

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REFERENCES Domingues, C.M., Church, J.A., White, N.J., Gleckler, P.J., Wijffels, S.E., Barker, P.M. and Dunn, J.R. 2008, Improved Estimates of Upper-Ocean Warming and Multi-Decadal SeaLevel Rise, Nature, 453, 1090-1093, doi:10.1038/nature07080. Gouretski, V. and Koltermann, K.P. 2007, How Much is the Ocean Really Warming?, Geophysical Research Letters, 34, L01610, doi:10.1029/2006GL027834. Ishii, M. and Kimoto, M. in press, Reevaluation of Historical Ocean Heat Content Variations with an XBT Depth Bias Correction, Journal of Oceanography. Levitus, S., Antonov, J.I., Wang, J., Delworth, T.L., Dixon, K.W. and Broccoli, A.J. 2001, Anthropogenic Warming of Earth’s Climate System, Science, 292(5515), 267-270 Lyman, J.M., Willis, J.K. and Johnson, G.C. 2006, Recent Cooling of the Upper Ocean. Geophysical Research Letters, 33(18), L18604, doi:10.1029/2006GL027033. Pielke, R.A., Sr. 2003, Heat Storage Within the Earth System, Bulletin of the American Meteorological Society, 84(3), 331-335, doi:10.1175/BAMS-84-3-331. Wijffels, S.E., Willis, J., Domingues, C.M., Barker, P., White, N.J., Gronell, A., Ridgway, K. and Church, J.A. 2008, Changing Expendable Bathythermograph Fall Rates and Their Impact on Estimates of Thermosteric Sea Level Rise, Journal of Climate, 21, 5657–5672, doi:10.1175/2008JCLI2290.1. Willis, J.K., Roemmich, D. and Cornuelle, B. 2004, Interannual Variability in Upper Ocean Heat Content, Temperature, and Thermosteric Expansion on Global Scales. Journal of Geophysical Research, 109(12), C12036, doi:10.1029/2003JC002260c. Willis, J.K., Lyman, J.M., Johnson, G.C. and Gilson, J. 2007, Correction to "Recent Cooling of the Upper Ocean," Geophysical Research Letters, 34(16), L16601, doi:10.1029/2007GL030323. Willis, J.K., Lyman, J.M., Johnson, G.C. and Gilson, J. 2008a, In Situ Data Biases and Recent Ocean Heat Content Variability, Journal of Atmospheric and Oceanic Technology, doi:10.1175/2008JTECHO608.1. Willis, J.K., Chambers, D.P. and Nerem, R.S. 2008b, Assessing the Globally Averaged Sea Level Budget on Seasonal to Interannual Timescales, Journal of Geophysical Research, 113, C06015, doi:10.1029/2007JC004517.

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