F. M. O'Connor,1 C. E. Johnson,1 O. Morgenstern,2,3 and W. J. Collins1. Received 13 May 2009; .... tropics. John and Soden [2007] extended that analysis to.
GEOPHYSICAL RESEARCH LETTERS, VOL. 36, L16801, doi:10.1029/2009GL039152, 2009
Interactions between tropospheric chemistry and climate model temperature and humidity biases F. M. O’Connor,1 C. E. Johnson,1 O. Morgenstern,2,3 and W. J. Collins1 Received 13 May 2009; accepted 7 July 2009; published 18 August 2009.
[1] Temperature and humidity climatologies from the Met Office Hadley Centre climate model, HadGAM1, show a strong cold bias of up to 5 K in the extra-tropical upper troposphere/lower stratosphere and a dry bias of up to 20% in the tropical lower and mid troposphere. Removing the temperature bias alone has little effect on tropospheric ozone or methane lifetime. Removing the humidity bias alone causes a reduction in both the global annual mean tropospheric ozone burden of greater than 2% and the methane lifetime of 3.6– 4.2%. The impact of removing both biases together is similar to that of removing the humidity bias alone. The choice of reanalysis product (ERA-40 or NCEP) to calculate the biases does not greatly affect the results. Radiative feedback from ozone and methane reduced some of the climate model biases without any significant change to the performance of the chemistry. Citation: O’Connor, F. M., C. E. Johnson, O. Morgenstern, and W. J. Collins (2009), Interactions between tropospheric chemistry and climate model temperature and humidity biases, Geophys. Res. Lett., 36, L16801, doi:10.1029/2009GL039152.
1. Introduction [2] Atmospheric chemistry, aerosols, and the biosphere are important features of the Earth System (ES). The role of feedbacks, such as chemistry-climate feedbacks, is being recognised [e.g., Johnson et al., 2001] and ES models will increasingly include aspects of atmospheric chemistry. However, the performance of an atmospheric chemistry module may differ when run in a chemical transport model (CTM) using prescribed meteorological analyses (e.g., NCEP and ECMWF) from that run in a chemistry-climate model. In addition, the inclusion of chemistry in a climate model may influence the model’s climatology when radiatively-active gases from the chemistry are passed to the radiation scheme. [3] The objectives of this work are two-fold. We aim to assess the impact of climate model biases on the performance of a tropospheric chemistry scheme and to explore how radiative feedback between the chemistry and climate will influence those biases. In Section 2, we will present the known climate model temperature and specific humidity biases. In Section 3, the impact of these biases on tropospheric chemistry and the impact of radiative feedback on climate model climatologies/biases will be presented.
1
Met Office Hadley Centre, Exeter, UK. NCAS-Climate-Chemistry, Chemistry Department, Cambridge University, Cambridge, UK. 3 Now at NIWA, Lauder, New Zealand. 2
Finally, a discussion of the results and concluding remarks will follow in Section 4.
2. Climate Model Biases and Experiments [4] The evaluation of the climate model temperature and specific humidity biases was carried out using a 10-year atmosphere-only integration of the HadGEM1 model [Johns et al., 2006], called HadGAM1, in which sea surface temperatures and sea ice datasets were taken from AMIP-II (www-pcmdi.llnl.gov/projects/amip). The experimental details were identical to those by Martin et al. [2006] except that the horizontal resolution used here was N48 (2.5° latitude 3.75° longitude). Modelled monthly mean climatologies were subsequently compared with monthly means from ERA-40 reanalyses [Uppala et al., 2005]. Figures 1a and 1b show the HadGAM1 temperature and humidity climatologies and Figures 1c and 1d show the corresponding biases relative to ERA-40. The HadGAM1 temperatures are too cold in the tropical upper troposphere by more than 2 K and in the extra-tropical upper troposphere/ lower stratosphere (UTLS) by more than 3 K. In the stratosphere, there are warm and cold biases of the order of 1 – 3 K. The stratospheric warm biases in both hemispheres are more extensive and larger by about 2 K in the June-July-August (JJA) period than in December-JanuaryFebruary (DJF) while the extra-tropical UTLS cold biases are stronger in the summer hemisphere than in the winter hemisphere by approximately 2 K. For humidity, the tropics are too dry by 10– 20% in the lower and mid troposphere with an even stronger dry bias (up to 50%) aloft. The extratropics exhibit a moist bias of up to 50% in the lower stratosphere with a weaker moist bias extending downwards into the troposphere equatorward of 50°. This extension, particularly in the southern hemisphere (20–50°S; 3–10 km), is stronger by a factor of 2 and more extensive in JJA than in DJF. Equally, the extra-tropical lower stratospheric moist bias in the northern hemisphere is stronger by a factor of 2 in JJA than in DJF. Although the biases are statistically significant at the 90% confidence interval, the ERA-40 climatologies themselves are not free from uncertainties; uncertainties in temperature and specific humidity are of the order of 1 K and 1.2 g/kg at 500 hPa and 850 hPa, respectively [Uppala et al., 2005]. Biases relative to NCEP/NCAR reanalyses [Kalnay et al., 1996] were also evaluated. The temperature biases are consistent with those relative to ERA-40. For humidity, HadGAM1’s dry bias of 10– 20% is more extensive (90°S – 90°N; 0 – 8 km) when compared with NCEP than ERA-40. However, NCEP humidity data above 500 hPa should be treated cautiously due to the use of only radiosonde-derived humidity [Paltridge et al., 2009].
Published in 2009 by the American Geophysical Union.
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Figure 1. Multi-annual zonal annual mean (a) temperature in K, (b) specific humidity in g kg 1, (c) HadGAM1-ERA40 absolute differences in temperature, and (d) HadGAM1-ERA40 relative differences in specific humidity from an N48L38 version of HadGAM1. The shaded regions in Figures 1c and 1d indicate where the biases in HadGAM1 relative to ERA-40 are statistically significant at the 90% confidence interval using Student’s t-test. The thick black dashed line in Figures 1c and 1d indicates the location of the tropopause, defined as the 380 K potential temperature surface in the tropics, the 2.0 pvu surface in the extra-tropics, and a weighted average of the two surfaces in the sub-tropics [Hoerling et al., 1993]. [5] Nevertheless, the biases in HadGAM1 are by no means unique to this climate model. Pierce et al. [2006], for example, found that most models in the third Coupled Model Intercomparison Project [Meehl et al., 2007] were too dry by 10 –25% below 800 hPa in the tropics and too moist by 25– 100% between 300 and 600 hPa in the extratropics. John and Soden [2007] extended that analysis to include temperature and comparisons with ERA-40. They concluded that climate models were too cold by 1 – 4 K throughout the troposphere with larger biases in the extratropics. The largest cold temperature bias (6 K) was located near 200 hPa in the extra-tropics of both hemispheres. HadGAM1’s strong cold and moist biases at 10 km in the extra-tropics were attributed by Martin et al. [2006] to incorrect positioning of the tropopause. Other studies suggest that errors in vertical transport [Pierce et al., 2006] or models’ advection schemes [Gates et al., 1999] could be responsible. Indeed, Stenke et al. [2008] demonstrated that the replacement of the advection scheme in the ECHAM4.L39 model resulted in a reduction in that model’s cold and moist biases. [6] Given that HadGAM1 has biases common to other climate models and that meteorological fields are potentially a significant source of difference between chemistry models [Stevenson et al., 2006], the objectives of this work are to assess the sensitivity of a tropospheric chemistry scheme to these biases and to explore the impact of tropospheric chemistry on model biases. We make use of a new community model called the United Kingdom Chemistry and Aerosols model, UKCA [Morgenstern et al., 2008, 2009; F. M. O’Connor et al., Evaluation of the new UKCA climate-composition model. Part II: The troposphere, manuscript in preparation, 2009]. The chemistry scheme used
here is one of two tropospheric schemes in UKCA and is described by Zeng and Pyle [2003]. Stratospheric ozone was assembled from Randel and Wu [1999], Randel et al. [1999], and Kiehl et al. [1999] and is prescribed approximately 3 – 5 km above the diagnosed tropopause [Hoerling et al., 1993]. For methane (CH4), an explicit loss term, scaled to give a global annual loss of 40 TgCH4/year, is applied to account for stratospheric removal. A full summary of the experiments carried out is shown in Table 1. In Runs 1 – 6, the bias correction was carried out at every time step using multi-annual monthly mean temperature and/or humidity differences. The underlying dynamics of the model were unchanged; i.e., the bias correction was seen only by the chemistry and the chemical fields themselves did not influence the radiation scheme. In Runs 7 and 8, no bias correction was applied but O3 and CH4 were allowed to feedback onto the circulation.
3. Chemistry-Biases Interactions [7] Multi-annual zonal annual mean distributions of ozone (O3) and hydroxyl (OH) radical from Run 1 are shown in Table 1. List of Model Experiments Run
Bias Correction
Radiative Feedback
1 2 3 4 5 6 7 8
None Temperature (ERA-40) Specific humidity (ERA-40) Temperature and specific humidity (ERA-40) Temperature (NCEP) Specific humidity (NCEP) None None
None None None None None None O3 O3, CH4
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Figures 2a and 2b. Results will primarily look at differences relative to these distributions. However, a more detailed description and evaluation of the tropospheric configuration of UKCA is given by F. M. O’Connor et al. (manuscript in preparation, 2009). 3.1. Impact of Temperature and Humidity Biases [8] When the temperature biases relative to ERA-40 are removed, this has the effect of altering the chemical reaction kinetics in Run 2 relative to Run 1. For example, an increase of 5 K at a temperature of 270 K causes an increase of 12% in the rate at which CH4 is oxidised by OH. The effect of removing the ERA-40 temperature biases (i.e., warming the UTLS region), however, has little impact on tropospheric OH abundance (Figure 2d) and the global tropospheric lifetime of CH4 against OH loss only changes from 9.97 ± 0.03 years in Run 1 to 9.93 ± 0.03 years in Run 2. Similarly, there was little impact on O3 production (
