Dynamic and thermodynamic changes in mean and extreme precipitation under changed climate
|
|
- Jane Douglas
- 5 years ago
- Views:
Transcription
1 GEOPHYSICAL RESEARCH LETTERS, VOL. 32,, doi: /2005gl023272, 2005 Dynamic and thermodynamic changes in mean and extreme precipitation under changed climate S. Emori National Institute for Environmental Studies, Tsukuba, Japan Frontier Research Center for Global Change, Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan S. J. Brown Hadley Centre for Climate Prediction and Research, Met Office, Exeter, UK Received 19 April 2005; revised 21 June 2005; accepted 26 July 2005; published 13 September [1] Extreme precipitation has been projected to increase more than the mean under future changed climate, but its mechanism is not clear. We have separated the dynamic and thermodynamic components of the mean and extreme precipitation changes projected in 6 climate model experiments. The dynamic change is due to the change in atmospheric motion, while the thermodynamic change is due to the change in atmospheric moisture content. The model results consistently show that there are areas with small change or decreases in the thermodynamic change for mean precipitation mainly over subtropics, while the thermodynamic change for extreme precipitation is an overall increase as a result of increased atmospheric moisture. The dynamic changes play a secondary role in the difference between mean and extreme and are limited to lower latitudes. Over many parts of mid- to high latitudes, mean and extreme precipitation increase in comparable magnitude due to a comparable thermodynamic increase. Citation: Emori, S., and S. J. Brown (2005), Dynamic and thermodynamic changes in mean and extreme precipitation under changed climate, Geophys. Res. Lett., 32,, doi: / 2005GL Introduction [2] It has been projected that precipitation extremes could increase by more than the (annual or seasonal) mean and may cause more frequent and severe floods in a future warmed climate [e.g., Cubasch et al., 2001]. Trenberth [1999] argued that enhancement of extreme precipitation is principally caused by enhancement of atmospheric moisture content, which feeds increased moisture to all weather systems. On a global mean basis, annual mean precipitation is constrained by the energy balance between atmospheric radiative cooling and latent heating, which is expected to limit mean precipitation increase to be lower than the rate of atmospheric moisture increase [Allen and Ingram, 2002]. On a regional basis, however, Emori et al. [2005] showed that areas where the changes in extremes are larger than those of the mean are limited to a few regions, rather than the whole globe. [3] The mechanism that causes larger increases in extremes than in the mean is not clear. For example, simplifying the argument by Trenberth [1999] to an idealized case by assuming that precipitation increases by the Published in 2005 by the American Geophysical Union. same percentage across all severity of events (following the fixed fractional uplift case of Jones et al. [1997]), the percentage change in extremes and the mean should be the same. To further clarify this relationship between the changes in mean and extreme precipitation under changed climate, we attempt to answer the question, to what degree we can attribute atmospheric moisture increase to the changes in precipitation, both for the mean and extremes. 2. Method [4] Daily mean 500 hpa vertical velocity (w) is taken as a proxy of the strength of dynamic disturbance at each grid point on each day. For example, in mid-latitudes winter, it is expected to correspond to the phase and strength of extratropical cyclones and anticyclones passing over the grid point of interest. For computational purposes, w is divided into bins with a uniform width of 50 hpa/day. Figure 1a shows an example of the relative frequency of occurrence for each w bin, regarded as probability density function (PDF) of w and denoted by Pr w, averaged over a particular region (equatorial central Pacific) obtained from the control run of CCSR/NIES/FRCGC AGCM. It has a peak around w = 0 and tails for both stronger upward and downward motion regimes. This shape of PDF is qualitatively the same for different regions from different models. Note that the positive w represents upward motion throughout this study, unlike the usual definition of pressure velocity, since positively correlated precipitation with upward vertical motion, as will be shown below, is to be preferred. Daily mean precipitation is then composited for each w bin to obtain the expected values of daily precipitation as a function of w at each grid point (P w ). Figure 1b shows an example of P w, for the same region and model as in Figure 1a. Precipitation is expected to be small over the downward motion regime (w < 0), while it is expected to be larger with stronger upward velocity over the upward motion regime (w > 0). A similar relationship is obtained for different regions from different model results, though the slope in the upward motion regime is dependent on the regions and also on the model used. Although Pr w and P w are dependent on seasons, the annual relationships are used in this initial study. [5] The annual mean precipitation P for a grid point can be represented by P ¼ P w Pr w dw: 1of5
2 EMORI AND BROWN: MEAN AND EXTREME PRECIPITATION CHANGES generally different from the 99th percentile value of w. The change in 99th percentile precipitation between changed and control climate can be expressed by dp 99 dw 99 * þ dpðw* dw * 99: ð2þ Figure 1. (a) Relative frequency of daily upward vertical motion over equatorial central Pacific (10 S 10 N, 180 E 210 E) in the control run of CCSR/NIES/FRCGC AGCM. (b) Composited daily precipitation for each daily vertical motion bin for the same region and the model as in (a); error bars denote one standard deviation of composited data. The change in mean precipitation between the changed and control climate, dp, can be expressed by dp ¼ P w dpr w dw þ dp w Pr w dw þ dp w dpr w dw: ð1þ The first term of the r.h.s is the change in mean precipitation due to the change in the PDF of w, that is, due to the change in the strength and/or frequency of dynamic disturbances. Hence, we call this term dynamic change. The second term is due to the change in the expected precipitation for given w. This term can be called non-dynamic or thermodynamic change and is taken to represent the change in atmospheric moisture content. The third represents the covariation term. This approach is basically that of Bony et al. [2004] for cloud-radiation analysis over the tropics. Note, however, that we use daily data and averages are taken only over time, while Bony et al. [2004] used monthly data and spatial as well as temporal averaging over the tropics. [6] A similar expression can be defined for the change in extreme precipitation. Here, we take multi-year mean of yearly 4th largest (approximately 99th percentile) value at each grid point as an index of extreme precipitation. Hereafter, we call this the 99th percentile precipitation and denote it by P 99. The corresponding w value (w* 99 ) can be obtained by inverting the relationship of P w, that is, * ¼ P ð Þ: w 99 w P 99 For inverting P w, a linear-interpolation is applied to the values between the representative values of the bins to obtain a continuous function of P w. Note that this value is The first term of the r.h.s is due to the change in extreme vertical velocity and is called dynamic change. The second term is due to the change in expected precipitation for extreme w fixed at the control value and is called thermodynamic change. The third represents the covariation term. Though the first and third terms are expressed with a linear approximation, they are actually evaluated with a finite difference approximation in the following analysis so that the equation holds precisely. 3. Models and Experiments [7] The models and experiments used in this study are summarized in Table 1. The coupled ocean-atmosphere climate models were obtained from the Program for Climate Model Diagnosis and Intercomparison (PCMDI) data archive established for the Intergovernmental Panel on Climate Change (IPCC) 4th Assessment Report. In addition, time-slice climate change experiments by two atmosphereonly models, CCSR/NIES/FRCGC AGCM [Emori et al., 2005] and HadAM3P [Rowell, 2005] are also used. The daily 500 hpa vertical velocity data for the coupled models are estimated from daily three-dimensional horizontal velocity data using the continuity equation, while those for the atmospheric models are direct output from the models. 4. Results [8] The temporal correlation at each grid point between daily precipitation and daily 500 hpa vertical velocity (positive upward) is higher than 0.5 over most parts of the globe in all the models examined. This fact supports the validity of the method described in Section 2. The correlation is relatively low over some subtropical regions with strong subsidence and over polar regions, where precipitation from clouds shallower than 500 hpa is expected to be dominant. In the following analysis, the areas where the correlation of at least one model is lower than 0.2 are masked out (shaded gray in Figures 2 and 3). [9] Figure 2 shows the multi-model ensemble mean of the total, dynamic and thermodynamic changes in annual mean precipitation defined by (1). They are shown in percentage change relative to the annual mean precipitation in the control experiments. Before the ensemble mean is taken, results from each model are scaled by the values of Table 1. Models and Experiments Model Resolution Global Mean Precipitation Change Control Experiment Climate Change Experiment MIROC3.2(hires) % 20C3M ( ) A1B ( ) MIROC3.2(medres) % 20C3M ( ) A1B ( ) GFDL-CM % 20C3M ( ) A1B ( ) MRI-CGCM2.3.2a % 20C3M ( ) A1B ( ) CCSR/NIES/FRCGC AGCM % Control ( ) 2 CO2 (20 years) HadAM3P % Control ( ) A2 ( ) 2of5
3 EMORI AND BROWN: MEAN AND EXTREME PRECIPITATION CHANGES Figure 2. Percentage change in annual mean precipitation due to climate change and its components: (a) total, (b) dynamic and (c) thermodynamic. global mean precipitation change listed in Table 1 to exclude the effects of different climate (hydrological) sensitivity of the models and of different scenarios in the case of time-slice runs. Also, all data is spatially interpolated to a T42 ( 2.81 ) grid before averaging. The total change (Figure 2a) shows general increase over tropics and midto high latitudes and decrease over some subtropical regions, a pattern commonly seen in previous studies [e.g., Cubasch et al., 2001]. The dynamic change (Figure 2b) partly explains the tropical Pacific increase and most of the subtropical decrease, while it is virtually zero over mid- to high latitudes (outside of 40 S to 40 N). The thermodynamic change (Figure 2c) explains almost all the mid- to high latitudes increase and part of the tropical increase. The covariation (figure not shown) is negligible except for an increase of up to 40% over equatorial central Pacific. The dominance of thermodynamic changes in extratropics and dynamic changes in lower latitudes is consistent with previous studies where the dynamic and thermodynamic changes of moisture transport and its convergence are discussed [e.g., Watterson, 1998]. [10] Figure 3 shows multi-model ensemble mean of the total, dynamic and thermodynamic changes in extreme Figure 3. Percentage change in the annual 4th largest daily precipitation (approximately 99th percentile) due to climate change and its components: (a) total, (b) dynamic and (c) thermodynamic. (99th percentile) daily precipitation defined by (2), in percentage relative to control values. The same scaling and interpolation procedures as for the mean precipitation have been applied before the ensemble mean is taken. Because of insufficient sampling of Pw for extreme cases, the separated dynamic and thermodynamic changes contain noise. To reduce this, we have applied a spatial filter once (Figures 3b and 3c), assuming the noise as random. The comparison between the total changes in mean and extreme precipitation (Figures 2a and 3a) is basically the same as was found by Emori et al. [2005] for the results of CCSR/NIES/FRCGC AGCM. That is, though their overall patterns are similar, there are some areas where the increase in the extreme is larger than that in the mean (or the mean is decreased). The global mean of the total changes in mean and extreme precipitation are 6.0% and 13.0%, respectively (note that data over masked areas are excluded). The pattern of dynamic change in the extreme is quite similar to the dynamic change in the mean (global mean of 4.3%, 4.4%, respectively, Figures 2b and 3b) though some regional difference can be identified. The thermodynamic change in the extreme shows overall increase, which is in 3 of 5
4 EMORI AND BROWN: MEAN AND EXTREME PRECIPITATION CHANGES remarkable contrast with the corresponding small changes or decreases in the mean particularly over the subtropics (global mean of 17.8%, 9.4%, respectively, Figures 2c and 3c). The difference in the total changes in mean and extreme precipitation can therefore be attributed mainly to the different thermodynamic change. The covariation for extremes is negligible (not shown). [11] The results of individual models are similar to the ensemble means described above. The largest model difference is over lower latitudes (30 S 30 N), where models respond differently to the various projected (or prescribed, in time-slice runs) sea surface temperature (SST) changes. The inter-model standard deviation of the total change either in the mean or in extreme is typically 10 30% (relative to control values) over the lower latitudes. Over the mid- to high latitudes, the inter-model standard deviation is typically smaller than 10%, suggesting that the present results are more robust for higher latitudes. 5. Concluding Discussion [12] The thermodynamic change in extreme precipitation has shown an overall increase (Figure 3c), which is due to the expected precipitation for given vertical motion, P w, increasing in the strong upward motion regime, regardless of geographical location. As suggested in Section 2, we consider that this is caused by increased atmospheric moisture content. This relation is supported by the fact that models with larger increases in global mean precipitable water (column integrated water vapor) tend to give larger global mean thermodynamic increases in extreme precipitation (the inter-model correlation coefficient is 0.85, which is significant at the 5% level). It is also interesting to note that increased P w at a given w means increased latent heating for a given upward motion. We found that, at least over the lower latitudes, this is balanced by increased adiabatic cooling due to enhanced dry static stability. [13] The thermodynamic change in mean precipitation (Figure 2c) has areas of modest change or decreasing values, mainly over the subtropics, unlike what was found for the extreme precipitation. This is because P w decreases or changes little in the downward and/or weak upward motion regime over these areas, in spite of the increased atmospheric moisture content. Although P w is small over the weak/downward motion regime and its change is also small, this is not negligible when integrated over whole w range (i.e., annual mean), because of the large statistical weight for this regime. It is this mechanism that keeps the percentage increase in global annual mean precipitation lower than that in global mean extreme precipitation. The decrease can be partly understood by considering the moisture budget of atmospheric column. When atmospheric moisture is increased, moisture divergence will be increased for a given lower tropospheric wind divergence, which would act to reduce P w in the downward motion regime, unless surface evaporation increases in compensation. [14] Over many parts of the mid- to high latitudes and tropics, the thermodynamic increase in the mean is as high as that in the extreme, resulting in the total increase in the both being comparable. These areas roughly correspond to areas with a mean upward motion in the control climate. Important exceptions are some tropical land areas (e.g., Amazon) and northern North Atlantic including the UK/Europe area. Further analysis is needed to clarify these regional details. [15] The dynamic terms play a secondary role in making difference between mean and extreme precipitation changes. The dynamic changes suggest that the frequency of strong upward motion is decreased over many parts of subtropics and is increased over the equatorial Pacific. Though this seems to be related to the changes in SST and atmospheric stability, more work is needed to clarify this. [16] The seasonal breakdown of the present analysis is desired for future work. The spread of composited data in constructing P w (error bars in Figure 1b) should be smaller in a seasonal relationship than in the annual, especially for higher latitudes, where P w can be considerably seasonally dependent. However, we have confirmed that seasonality does not seriously affect the annual results of the present study. For the extreme precipitation, it is because extremely strong upward motions (w* 99 ) occur mostly accompanying extreme precipitation events (P 99 ) in wet seasons and seldom occur in dry seasons. That is, for an extremely strong upward motion, the annual P w approximately represents that of the wet seasons alone. [17] The present analysis relies on the modeled P w and PDF of w. At present, there seems to be no good way to validate these functions, since daily vertical velocities of re-analysis data seem strongly dependent on the models used in the reanalysis and does not always correspond well to the observed daily precipitation. The quantitative validation of this analysis remains for future work. Nevertheless, the result of this study is qualitatively robust among all the models examined. [18] Acknowledgments. This work was done whilst the first author (SE) was at the Hadley Centre as a visiting scientist. We thank people at the Hadley Centre as well as the K-1 Japan project members for support and discussion. Thanks are extended to Ian Watterson, Isaac Held, Julia Slingo, Chris Ferro, Richard Jones, Myles Allen, William Ingram, Pardeep Pall and an anonymous reviewer for helpful comments and discussion. We also acknowledge the international modeling groups for providing their data for analysis, the PCMDI for collecting and archiving the model data, the JSC/ CLIVAR Working Group on Coupled Modelling (WGCM) and their Coupled Model Intercomparison Project (CMIP) and Climate Simulation Panel for organizing the model data analysis activity, and the IPCC WG1 TSU for technical support. The IPCC Data Archive at Lawrence Livermore National Laboratory is supported by the Office of Science, U.S. Department of Energy. This work was partially supported by the Research Revolution 2002 (RR2002) of the Ministry of Education, Sports, Culture, Science and Technology of Japan, by the Global Environment Research Fund (GERF) of the Ministry of the Environment of Japan, and by the U.K. Department of the Environment, Food and Rural Affairs (Contract PECD/7/12/37). The model calculations of MIROC3.2 and CCSR/NIES/FRCGC AGCM were made on the Earth Simulator. The GFD-DENNOU Library was used for the drawings. References Allen, M., and W. Ingram (2002), Constraints on future changes in climate and the hydrologic cycle, Nature, 419, Bony, S., J.-L. Dufresne, H. Le Treut, J.-J. Morcrette, and C. Senior (2004), On dynamic and thermodynamic components of cloud changes, Clim. Dyn., 22, Cubasch, U., G. A. Meehl, G. J. Boer, R. J. Stouffer, M. Dix, A. Noda, C. A. Senior, S. Raper, and K. S. Yap (2001), Projections of future climate change, in Climate Change 2001: The Scientific Basis, editedbyj.t. Houghton et al., pp , Cambridge Univ. Press, New York. Emori, S., A. Hasegawa, T. Suzuki, and K. Dairaku (2005), Validation, parameterization dependence, and future projection of daily precipitation simulated with a high-resolution atmospheric GCM, Geophys. Res. Lett., 32, L06708, doi: /2004gl of5
5 EMORI AND BROWN: MEAN AND EXTREME PRECIPITATION CHANGES Jones, R. G., J. M. Murphy, M. Moguer, and A. B. Keen (1997), Simulations of climate change over Europe using a nested regional-climate model. II: Comparison of driving and regional model responses to a doubling of carbon dioxide, Q. J. R. Meteorol. Soc., 123, Rowell, D. P. (2005), A scenario of European climate change for the late 21st century: Seasonal means and interannual variability, Clim. Dyn., in press. Trenberth, K. E. (1999), Conceptual framework for changes of extremes of the hydrological cycle with climate change, Clim. Change, 42, Watterson, I. G. (1998), An analysis of the global water cycle of present and doubled CO2 climates simulated by the CSIRO general circulation model, J. Geophys. Res., 103, 23,113 23,129. S. J. Brown, Hadley Centre, Met Office, FitzRoy Road, Exeter, EX1 3PB, UK. (simon.brown@metoffice.gov.uk) S. Emori, National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki , Japan. (emori@nies.go.jp) 5of5
FUTURE PROJECTIONS OF PRECIPITATION CHARACTERISTICS IN ASIA
FUTURE PROJECTIONS OF PRECIPITATION CHARACTERISTICS IN ASIA AKIO KITOH, MASAHIRO HOSAKA, YUKIMASA ADACHI, KENJI KAMIGUCHI Meteorological Research Institute Tsukuba, Ibaraki 305-0052, Japan It is anticipated
More informationHigh initial time sensitivity of medium range forecasting observed for a stratospheric sudden warming
GEOPHYSICAL RESEARCH LETTERS, VOL. 37,, doi:10.1029/2010gl044119, 2010 High initial time sensitivity of medium range forecasting observed for a stratospheric sudden warming Yuhji Kuroda 1 Received 27 May
More informationDecreasing trend of tropical cyclone frequency in 228-year high-resolution AGCM simulations
GEOPHYSICAL RESEARCH LETTERS, VOL. 39,, doi:10.1029/2012gl053360, 2012 Decreasing trend of tropical cyclone frequency in 228-year high-resolution AGCM simulations Masato Sugi 1,2 and Jun Yoshimura 2 Received
More informationAn Initial Estimate of the Uncertainty in UK Predicted Climate Change Resulting from RCM Formulation
An Initial Estimate of the Uncertainty in UK Predicted Climate Change Resulting from RCM Formulation Hadley Centre technical note 49 David P. Rowell 6 May2004 An Initial Estimate of the Uncertainty in
More informationCold months in a warming climate
GEOPHYSICAL RESEARCH LETTERS, VOL. 38,, doi:10.1029/2011gl049758, 2011 Cold months in a warming climate Jouni Räisänen 1 and Jussi S. Ylhäisi 1 Received 21 September 2011; revised 18 October 2011; accepted
More informationDevelopment of a Coupled Atmosphere-Ocean-Land General Circulation Model (GCM) at the Frontier Research Center for Global Change
Chapter 1 Atmospheric and Oceanic Simulation Development of a Coupled Atmosphere-Ocean-Land General Circulation Model (GCM) at the Frontier Research Center for Global Change Project Representative Tatsushi
More informationThe ENSEMBLES Project
The ENSEMBLES Project Providing ensemble-based predictions of climate changes and their impacts by Dr. Chris Hewitt Abstract The main objective of the ENSEMBLES project is to provide probabilistic estimates
More informationENSO amplitude changes in climate change commitment to atmospheric CO 2 doubling
GEOPHYSICAL RESEARCH LETTERS, VOL. 33, L13711, doi:10.1029/2005gl025653, 2006 ENSO amplitude changes in climate change commitment to atmospheric CO 2 doubling Sang-Wook Yeh, 1 Young-Gyu Park, 1 and Ben
More information1. Header Land-Atmosphere Predictability Using a Multi-Model Strategy Paul A. Dirmeyer (PI) Zhichang Guo (Co-I) Final Report
1. Header Land-Atmosphere Predictability Using a Multi-Model Strategy Paul A. Dirmeyer (PI) Zhichang Guo (Co-I) Final Report 2. Results and Accomplishments Output from multiple land surface schemes (LSS)
More informationProjection of Extreme Wave Climate Change under Global Warming
Hydrological Research Letters, 4, 15 19 (2010) Published online in J-STAGE (www.jstage.jst.go.jp/browse/hrl). DOI: 10.3178/HRL.4.15 Projection of Extreme Wave Climate Change under Global Warming Nobuhito
More informationLink between land-ocean warming contrast and surface relative humidities in simulations with coupled climate models
Link between land-ocean warming contrast and surface relative humidities in simulations with coupled climate models The MIT Faculty has made this article openly available. Please share how this access
More informationSUPPLEMENTARY INFORMATION
Figure S1. Summary of the climatic responses to the Gulf Stream. On the offshore flank of the SST front (black dashed curve) of the Gulf Stream (green long arrow), surface wind convergence associated with
More informationMore extreme precipitation in the world s dry and wet regions
More extreme precipitation in the world s dry and wet regions Markus G. Donat, Andrew L. Lowry, Lisa V. Alexander, Paul A. O Gorman, Nicola Maher Supplementary Table S1: CMIP5 simulations used in this
More informationAttribution of anthropogenic influence on seasonal sea level pressure
Click Here for Full Article GEOPHYSICAL RESEARCH LETTERS, VOL. 36, L23709, doi:10.1029/2009gl041269, 2009 Attribution of anthropogenic influence on seasonal sea level pressure N. P. Gillett 1 and P. A.
More informationBoundary layer equilibrium [2005] over tropical oceans
Boundary layer equilibrium [2005] over tropical oceans Alan K. Betts [akbetts@aol.com] Based on: Betts, A.K., 1997: Trade Cumulus: Observations and Modeling. Chapter 4 (pp 99-126) in The Physics and Parameterization
More informationSupplementary Figure 1 Observed change in wind and vertical motion. Anomalies are regime differences between periods and obtained
Supplementary Figure 1 Observed change in wind and vertical motion. Anomalies are regime differences between periods 1999 2013 and 1979 1998 obtained from ERA-interim. Vectors are horizontal wind at 850
More informationAnalysis of variability and trends of extreme rainfall events over India using 104 years of gridded daily rainfall data
Click Here for Full Article GEOPHYSICAL RESEARCH LETTERS, VOL. 35, L18707, doi:10.1029/2008gl035143, 2008 Analysis of variability and trends of extreme rainfall events over India using 104 years of gridded
More informationProjections of future climate change
Projections of future climate change Matthew Collins 1,2 and Catherine A. Senior 2 1 Centre for Global Atmospheric Modelling, Department of Meteorology, University of Reading 2 Met Office Hadley Centre,
More informationSimulated variability in the mean atmospheric meridional circulation over the 20th century
GEOPHYSICAL RESEARCH LETTERS, VOL. 36, L06704, doi:10.1029/2008gl036741, 2009 Simulated variability in the mean atmospheric meridional circulation over the 20th century Damianos F. Mantsis 1 and Amy C.
More informationContents of this file
Geophysical Research Letters Supporting Information for Future changes in tropical cyclone activity in high-resolution large-ensemble simulations Kohei Yoshida 1, Masato Sugi 1, Ryo Mizuta 1, Hiroyuki
More informationThe importance of including variability in climate
D. M. H. Sexton and G. R. Harris SUPPLEMENTARY INFORMATION The importance of including variability in climate change projections used for adaptation Modification to time scaling to allow for short-term
More informationHow closely do changes in surface and column water vapor follow Clausius-Clapeyron scaling in climate change simulations?
How closely do changes in surface and column water vapor follow Clausius-Clapeyron scaling in climate change simulations? The MIT Faculty has made this article openly available. Please share how this access
More informationHuman influence on terrestrial precipitation trends revealed by dynamical
1 2 3 Supplemental Information for Human influence on terrestrial precipitation trends revealed by dynamical adjustment 4 Ruixia Guo 1,2, Clara Deser 1,*, Laurent Terray 3 and Flavio Lehner 1 5 6 7 1 Climate
More informationAnthropogenic warming of central England temperature
ATMOSPHERIC SCIENCE LETTERS Atmos. Sci. Let. 7: 81 85 (2006) Published online 18 September 2006 in Wiley InterScience (www.interscience.wiley.com).136 Anthropogenic warming of central England temperature
More informationClimate Dynamics (PCC 587): Hydrologic Cycle and Global Warming
Climate Dynamics (PCC 587): Hydrologic Cycle and Global Warming D A R G A N M. W. F R I E R S O N U N I V E R S I T Y O F W A S H I N G T O N, D E P A R T M E N T O F A T M O S P H E R I C S C I E N C
More informationHadley Centre for Climate Prediction and Research, Met Office, FitzRoy Road, Exeter, EX1 3PB, UK.
Temperature Extremes, the Past and the Future. S Brown, P Stott, and R Clark Hadley Centre for Climate Prediction and Research, Met Office, FitzRoy Road, Exeter, EX1 3PB, UK. Tel: +44 (0)1392 886471 Fax
More informationMEAN CLIMATE AND ANNUAL CYCLE IN A REGIONAL CLIMATE CHANGE EXPERIMENT OVER SOUTHERN SOUTH AMERICA. II: CLIMATE CHANGE SCENARIOS ( ).
MEAN CLIMATE AND ANNUAL CYCLE IN A REGIONAL CLIMATE CHANGE EXPERIMENT OVER SOUTHERN SOUTH AMERICA. II: CLIMATE CHANGE SCENARIOS (2081-2090). Mario N. Nuñez*, Silvina Solman and María Fernanda Cabré Centro
More informationClimate change uncertainty for daily minimum and maximum temperatures: A model inter-comparison
Click Here for Full Article GEOPHYSICAL RESEARCH LETTERS, VOL. 34, L05715, doi:10.1029/2006gl028726, 2007 Climate change uncertainty for daily minimum and maximum temperatures: A model inter-comparison
More informationAn Introduction to Coupled Models of the Atmosphere Ocean System
An Introduction to Coupled Models of the Atmosphere Ocean System Jonathon S. Wright jswright@tsinghua.edu.cn Atmosphere Ocean Coupling 1. Important to climate on a wide range of time scales Diurnal to
More informationConsistent changes in twenty-first century daily precipitation from regional climate simulations for Korea using two convection parameterizations
Click Here for Full Article GEOPHYSICAL RESEARCH LETTERS, VOL. 35, L14706, doi:10.1029/2008gl034126, 2008 Consistent changes in twenty-first century daily precipitation from regional climate simulations
More informationA last saturation diagnosis of subtropical water vapor response to global warming
Click Here for Full Article GEOPHYSICAL RESEARCH LETTERS, VOL. 37,, doi:10.1029/2009gl042316, 2010 A last saturation diagnosis of subtropical water vapor response to global warming John V. Hurley 1 and
More informationREQUEST FOR A SPECIAL PROJECT
REQUEST FOR A SPECIAL PROJECT 2017 2019 MEMBER STATE: Sweden.... 1 Principal InvestigatorP0F P: Wilhelm May... Affiliation: Address: Centre for Environmental and Climate Research, Lund University Sölvegatan
More informationForcing, feedbacks and climate sensitivity in CMIP5 coupled atmosphere-ocean climate models
GEOPHYSICAL RESEARCH LETTERS, VOL. 39,, doi:10.1029/2012gl051607, 2012 Forcing, feedbacks and climate sensitivity in CMIP5 coupled atmosphere-ocean climate models Timothy Andrews, 1 Jonathan M. Gregory,
More informationDoes the model regional bias affect the projected regional climate change? An analysis of global model projections
Climatic Change (21) 1:787 795 DOI 1.17/s1584-1-9864-z LETTER Does the model regional bias affect the projected regional climate change? An analysis of global model projections A letter Filippo Giorgi
More informationTHE RELATION AMONG SEA ICE, SURFACE TEMPERATURE, AND ATMOSPHERIC CIRCULATION IN SIMULATIONS OF FUTURE CLIMATE
THE RELATION AMONG SEA ICE, SURFACE TEMPERATURE, AND ATMOSPHERIC CIRCULATION IN SIMULATIONS OF FUTURE CLIMATE Bitz, C. M., Polar Science Center, University of Washington, U.S.A. Introduction Observations
More informationThe ozone hole indirect effect: Cloud-radiative anomalies accompanying the poleward shift of the eddy-driven jet in the Southern Hemisphere
GEOPHYSICAL RESEARCH LETTERS, VOL. 4, 388 392, doi:1.12/grl.575, 213 The ozone hole indirect effect: Cloud-radiative anomalies accompanying the poleward shift of the eddy-driven jet in the Southern Hemisphere
More informationProjection of Ocean Wave Climate Change Based on Numerical Simulations
Projection of Ocean Wave Climate Change Based on Numerical Simulations Muhammad Zikra a,*, Noriaki Hashimoto b and Kodama Mitsuyasu b a) Department of Ocean Engineering, Faculty of Marine Technology, Institut
More informationHow Will Low Clouds Respond to Global Warming?
How Will Low Clouds Respond to Global Warming? By Axel Lauer & Kevin Hamilton CCSM3 UKMO HadCM3 UKMO HadGEM1 iram 2 ECHAM5/MPI OM 3 MIROC3.2(hires) 25 IPSL CM4 5 INM CM3. 4 FGOALS g1. 7 GISS ER 6 GISS
More informationNOTES AND CORRESPONDENCE The Skillful Time Scale of Climate Models
Journal January of 2016 the Meteorological Society of Japan, I. TAKAYABU Vol. 94A, pp. and 191 197, K. HIBINO 2016 191 DOI:10.2151/jmsj.2015-038 NOTES AND CORRESPONDENCE The Skillful Time Scale of Climate
More informationObserved Trends in Wind Speed over the Southern Ocean
GEOPHYSICAL RESEARCH LETTERS, VOL. 39,, doi:10.1029/2012gl051734, 2012 Observed s in over the Southern Ocean L. B. Hande, 1 S. T. Siems, 1 and M. J. Manton 1 Received 19 March 2012; revised 8 May 2012;
More informationFuture pattern of Asian drought under global warming scenario
Future pattern of Asian drought under global warming scenario Kim D.W., Byun H.R., Lee S.M. in López-Francos A. (ed.). Drought management: scientific and technological innovations Zaragoza : CIHEAM Options
More informationSUPPLEMENTARY INFORMATION
SUPPLEMENTARY INFORMATION DOI: 10.1038/NGEO1854 Anthropogenic aerosol forcing of Atlantic tropical storms N. J. Dunstone 1, D. S. Smith 1, B. B. B. Booth 1, L. Hermanson 1, R. Eade 1 Supplementary information
More informationRobust Arctic sea-ice influence on the frequent Eurasian cold winters in past decades
SUPPLEMENTARY INFORMATION DOI: 10.1038/NGEO2277 Robust Arctic sea-ice influence on the frequent Eurasian cold winters in past decades Masato Mori 1*, Masahiro Watanabe 1, Hideo Shiogama 2, Jun Inoue 3,
More informationJune 1993 T. Nitta and J. Yoshimura 367. Trends and Interannual and Interdecadal Variations of. Global Land Surface Air Temperature
June 1993 T. Nitta and J. Yoshimura 367 Trends and Interannual and Interdecadal Variations of Global Land Surface Air Temperature By Tsuyoshi Nitta Center for Climate System Research, University of Tokyo,
More informationMoist static energy budget diagnostics for. monsoon research. H. Annamalai
Moist static energy budget diagnostics for monsoon research H. Annamalai JJAS Precipitation and SST Climatology I III II Multiple regional heat sources - EIO and SPCZ still experience high precipitation
More informationLow-level wind, moisture, and precipitation relationships near the South Pacific Convergence Zone in CMIP3/CMIP5 models
Low-level wind, moisture, and precipitation relationships near the South Pacific Convergence Zone in CMIP3/CMIP5 models Matthew J. Niznik and Benjamin R. Lintner Rutgers University 25 April 2012 niznik@envsci.rutgers.edu
More informationImpact of sea surface temperatures on African climate. Alessandra Giannini
Impact of sea surface temperatures on African climate Alessandra Giannini alesall@iri.columbia.edu Outline: Intro/Motivation: demand-driven science, use of seasonal climate prediction, adaptation to climate
More informationSpatiotemporal patterns of changes in maximum and minimum temperatures in multi-model simulations
Click Here for Full Article GEOPHYSICAL RESEARCH LETTERS, VOL. 36, L02702, doi:10.1029/2008gl036141, 2009 Spatiotemporal patterns of changes in maximum and minimum temperatures in multi-model simulations
More information4.4 EVALUATION OF AN IMPROVED CONVECTION TRIGGERING MECHANISM IN THE NCAR COMMUNITY ATMOSPHERE MODEL CAM2 UNDER CAPT FRAMEWORK
. EVALUATION OF AN IMPROVED CONVECTION TRIGGERING MECHANISM IN THE NCAR COMMUNITY ATMOSPHERE MODEL CAM UNDER CAPT FRAMEWORK Shaocheng Xie, James S. Boyle, Richard T. Cederwall, and Gerald L. Potter Atmospheric
More informationThe Interannual Relationship between the Latitude of the Eddy-Driven Jet and the Edge of the Hadley Cell
15 JANUARY 2011 K A N G A N D P O L V A N I 563 The Interannual Relationship between the Latitude of the Eddy-Driven Jet and the Edge of the Hadley Cell SARAH M. KANG Department of Applied Physics and
More informationHow might extratropical storms change in the future? Len Shaffrey National Centre for Atmospheric Science University of Reading
How might extratropical storms change in the future? Len Shaffrey National Centre for Atmospheric Science University of Reading Extratropical storms Extratropical storms Strong winds, extreme waves, storm
More informationparticular regional weather extremes
SUPPLEMENTARY INFORMATION DOI: 1.138/NCLIMATE2271 Amplified mid-latitude planetary waves favour particular regional weather extremes particular regional weather extremes James A Screen and Ian Simmonds
More informationWhich Climate Model is Best?
Which Climate Model is Best? Ben Santer Program for Climate Model Diagnosis and Intercomparison Lawrence Livermore National Laboratory, Livermore, CA 94550 Adapting for an Uncertain Climate: Preparing
More informationTROPICAL-EXTRATROPICAL INTERACTIONS
Notes of the tutorial lectures for the Natural Sciences part by Alice Grimm Fourth lecture TROPICAL-EXTRATROPICAL INTERACTIONS Anomalous tropical SST Anomalous convection Anomalous latent heat source Anomalous
More informationDeep ocean heat uptake as a major source of spread in transient climate change simulations
Click Here for Full Article GEOPHYSICAL RESEARCH LETTERS, VOL. 36, L22701, doi:10.1029/2009gl040845, 2009 Deep ocean heat uptake as a major source of spread in transient climate change simulations J. Boé,
More informationClimate Change Scenario, Climate Model and Future Climate Projection
Training on Concept of Climate Change: Impacts, Vulnerability, Adaptation and Mitigation 6 th December 2016, CEGIS, Dhaka Climate Change Scenario, Climate Model and Future Climate Projection A.K.M. Saiful
More informationSUPPLEMENTARY INFORMATION
Intensification of Northern Hemisphere Subtropical Highs in a Warming Climate Wenhong Li, Laifang Li, Mingfang Ting, and Yimin Liu 1. Data and Methods The data used in this study consists of the atmospheric
More informationTropical stratospheric zonal winds in ECMWF ERA-40 reanalysis, rocketsonde data, and rawinsonde data
GEOPHYSICAL RESEARCH LETTERS, VOL. 32, L09806, doi:10.1029/2004gl022328, 2005 Tropical stratospheric zonal winds in ECMWF ERA-40 reanalysis, rocketsonde data, and rawinsonde data Mark P. Baldwin Northwest
More informationHow large are projected 21st century storm track changes?
How large are projected 21st century storm track changes? Article Published Version Open Access (OnlineOpen) Harvey, B. J., Shaffrey, L. C., Woollings, T. J., Zappa, G. and Hodges, K. I. (2012) How large
More informationSecond-Order Draft Chapter 10 IPCC WG1 Fourth Assessment Report
Second-Order Draft Chapter IPCC WG Fourth Assessment Report Figure... Multi model mean changes in a) zonal mean cloud fraction (in %), shown as a cross section though the atmosphere, and b) total cloud
More informationCharacteristics of Storm Tracks in JMA s Seasonal Forecast Model
Characteristics of Storm Tracks in JMA s Seasonal Forecast Model Akihiko Shimpo 1 1 Climate Prediction Division, Japan Meteorological Agency, Japan Correspondence: ashimpo@naps.kishou.go.jp INTRODUCTION
More informationTwenty-first-century projections of North Atlantic tropical storms from CMIP5 models
SUPPLEMENTARY INFORMATION DOI: 10.1038/NCLIMATE1530 Twenty-first-century projections of North Atlantic tropical storms from CMIP5 models SUPPLEMENTARY FIGURE 1. Annual tropical Atlantic SST anomalies (top
More informationSUPPLEMENTARY INFORMATION
Effect of remote sea surface temperature change on tropical cyclone potential intensity Gabriel A. Vecchi Geophysical Fluid Dynamics Laboratory NOAA Brian J. Soden Rosenstiel School for Marine and Atmospheric
More informationWhat kind of stratospheric sudden warming propagates to the troposphere?
What kind of stratospheric sudden warming propagates to the troposphere? Ken I. Nakagawa 1, and Koji Yamazaki 2 1 Sapporo District Meteorological Observatory, Japan Meteorological Agency Kita-2, Nishi-18,
More informationClimate Modeling and Downscaling
Climate Modeling and Downscaling Types of climate-change experiments: a preview 1) What-if sensitivity experiments increase the optically active gases and aerosols according to an assumed scenario, and
More informationEvaluation of short-term climate change prediction in multi-model CMIP5 decadal hindcasts
GEOPHYSICAL RESEARCH LETTERS, VOL. 39,, doi:10.1029/2012gl051644, 2012 Evaluation of short-term climate change prediction in multi-model CMIP5 decadal hindcasts Hye-Mi Kim, 1 Peter J. Webster, 1 and Judith
More informationChapter outline. Reference 12/13/2016
Chapter 2. observation CC EST 5103 Climate Change Science Rezaul Karim Environmental Science & Technology Jessore University of science & Technology Chapter outline Temperature in the instrumental record
More informationDo global warming targets limit heatwave risk?
GEOPHYSICAL RESEARCH LETTERS, VOL. 37,, doi:10.1029/2010gl043898, 2010 Do global warming targets limit heatwave risk? Robin T. Clark, 1 James M. Murphy, 1 and Simon J. Brown 1 Received 11 May 2010; revised
More informationLecture 7: The Monash Simple Climate
Climate of the Ocean Lecture 7: The Monash Simple Climate Model Dr. Claudia Frauen Leibniz Institute for Baltic Sea Research Warnemünde (IOW) claudia.frauen@io-warnemuende.de Outline: Motivation The GREB
More information1. Introduction. In following sections, a more detailed description of the methodology is provided, along with an overview of initial results.
7B.2 MODEL SIMULATED CHANGES IN TC INTENSITY DUE TO GLOBAL WARMING Kevin A. Hill*, Gary M. Lackmann, and A. Aiyyer North Carolina State University, Raleigh, North Carolina 1. Introduction The impact of
More informationInfluence of eddy driven jet latitude on North Atlantic jet persistence and blocking frequency in CMIP3 integrations
GEOPHYSICAL RESEARCH LETTERS, VOL. 37,, doi:10.1029/2010gl045700, 2010 Influence of eddy driven jet latitude on North Atlantic jet persistence and blocking frequency in CMIP3 integrations Elizabeth A.
More informationClimate Risk Profile for Samoa
Climate Risk Profile for Samoa Report Prepared by Wairarapa J. Young Samoa Meteorology Division March, 27 Summary The likelihood (i.e. probability) components of climate-related risks in Samoa are evaluated
More informationSpatial patterns of probabilistic temperature change projections from a multivariate Bayesian analysis
Zurich Open Repository and Archive University of Zurich Main Library Strickhofstrasse 39 CH-8057 Zurich www.zora.uzh.ch Year: 2007 Spatial patterns of probabilistic temperature change projections from
More information1990 Intergovernmental Panel on Climate Change Impacts Assessment
1990 Intergovernmental Panel on Climate Change Impacts Assessment Although the variability of weather and associated shifts in the frequency and magnitude of climate events were not available from the
More informationSUPPLEMENTARY INFORMATION
SUPPLEMENTARY INFORMATION DOI: 1.138/NGEO1799 Robust direct effect of carbon dioxide on tropical circulation and regional precipitation Sandrine Bony 1,, Gilles Bellon 2, Daniel Klocke 3, Steven Sherwood
More informationClimate Modeling Dr. Jehangir Ashraf Awan Pakistan Meteorological Department
Climate Modeling Dr. Jehangir Ashraf Awan Pakistan Meteorological Department Source: Slides partially taken from A. Pier Siebesma, KNMI & TU Delft Key Questions What is a climate model? What types of climate
More informationControl of land-ocean temperature contrast by ocean heat uptake
Click Here for Full Article GEOPHYSICAL RESEARCH LETTERS, VOL. 34, L3704, doi:0.029/2007gl029755, 2007 Control of land-ocean temperature contrast by ocean heat uptake F. Hugo Lambert and John C. H. Chiang
More informationThe Two Types of ENSO in CMIP5 Models
1 2 3 The Two Types of ENSO in CMIP5 Models 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 Seon Tae Kim and Jin-Yi Yu * Department of Earth System
More information3.4 THE IMPACT OF CONVECTIVE PARAMETERIZATION SCHEMES ON CLIMATE SENSITIVITY
3.4 THE IMPACT OF CONVECTIVE PARAMETERIZATION SCHEMES ON CLIMATE SENSITIVITY David J. Karoly*, Lance M. Leslie and Diandong Ren School of Meteorology, University of Oklahoma, Norman OK and Mark Leplastrier
More informationConsequences for Climate Feedback Interpretations
CO 2 Forcing Induces Semi-direct Effects with Consequences for Climate Feedback Interpretations Timothy Andrews and Piers M. Forster School of Earth and Environment, University of Leeds, Leeds, LS2 9JT,
More informationImpacts of Climate Change on Autumn North Atlantic Wave Climate
Impacts of Climate Change on Autumn North Atlantic Wave Climate Will Perrie, Lanli Guo, Zhenxia Long, Bash Toulany Fisheries and Oceans Canada, Bedford Institute of Oceanography, Dartmouth, NS Abstract
More informationClimate model simulations of the observed early-2000s hiatus of global warming
Climate model simulations of the observed early-2000s hiatus of global warming Gerald A. Meehl 1, Haiyan Teng 1, and Julie M. Arblaster 1,2 1. National Center for Atmospheric Research, Boulder, CO 2. CAWCR,
More informationNonlinear atmospheric response to Arctic sea-ice loss under different sea ice scenarios
Nonlinear atmospheric response to Arctic sea-ice loss under different sea ice scenarios Hans Chen, Fuqing Zhang and Richard Alley Advanced Data Assimilation and Predictability Techniques The Pennsylvania
More informationThe two types of ENSO in CMIP5 models
GEOPHYSICAL RESEARCH LETTERS, VOL. 39,, doi:10.1029/2012gl052006, 2012 The two types of ENSO in CMIP5 models Seon Tae Kim 1 and Jin-Yi Yu 1 Received 12 April 2012; revised 14 May 2012; accepted 15 May
More informationAppendix 1: UK climate projections
Appendix 1: UK climate projections The UK Climate Projections 2009 provide the most up-to-date estimates of how the climate may change over the next 100 years. They are an invaluable source of information
More informationWater Vapor and the Dynamics of Climate Changes
Water Vapor and the Dynamics of Climate Changes Tapio Schneider California Institute of Technology (based on Rev. Geophys. article with Xavier Levine and Paul O Gorman) Water vapor dynamics in warming
More informationSupplementary Information for:
Supplementary Information for: Linkage between global sea surface temperature and hydroclimatology of a major river basin of India before and after 1980 P. Sonali, Ravi S. Nanjundiah, & D. Nagesh Kumar
More informationIntroduction to Climate ~ Part I ~
2015/11/16 TCC Seminar JMA Introduction to Climate ~ Part I ~ Shuhei MAEDA (MRI/JMA) Climate Research Department Meteorological Research Institute (MRI/JMA) 1 Outline of the lecture 1. Climate System (
More informationHemispherical Asymmetry of Tropical Precipitation in ECHAM5/MPI-OM during El Niño and under Global Warming
15 MARCH 2008 C H O U A N D T U 1309 Hemispherical Asymmetry of Tropical Precipitation in ECHAM5/MPI-OM during El Niño and under Global Warming CHIA CHOU Research Center for Environmental Changes, Academia
More informationInternational Journal of Scientific and Research Publications, Volume 3, Issue 5, May ISSN
International Journal of Scientific and Research Publications, Volume 3, Issue 5, May 2013 1 Projection of Changes in Monthly Climatic Variability at Local Level in India as Inferred from Simulated Daily
More informationGlobal Warming: The known, the unknown, and the unknowable
Global Warming: The known, the unknown, and the unknowable Barry A. Klinger Jagadish Shukla George Mason University (GMU) Institute of Global Environment and Society (IGES) January, 2008, George Mason
More informationChapter 10: Global Climate Projections
0 0 Chapter 0: Global Climate Projections Coordinating Lead Authors: Gerald A. Meehl, Thomas F. Stocker Lead Authors: William Collins, Pierre Friedlingstein, Amadou Gaye, Jonathan Gregory, Akio Kitoh,
More informationCoupling between Arctic feedbacks and changes in poleward energy transport
GEOPHYSICAL RESEARCH LETTERS, VOL. 38,, doi:10.1029/2011gl048546, 2011 Coupling between Arctic feedbacks and changes in poleward energy transport Yen Ting Hwang, 1 Dargan M. W. Frierson, 1 and Jennifer
More informationThe Climate Sensitivity of the Community Climate System Model Version 3 (CCSM3)
2584 J O U R N A L O F C L I M A T E VOLUME 19 The Climate Sensitivity of the Community Climate System Model Version 3 (CCSM3) JEFFREY T. KIEHL, CHRISTINE A. SHIELDS, JAMES J. HACK, AND WILLIAM D. COLLINS
More informationGlobal warming and Extremes of Weather. Prof. Richard Allan, Department of Meteorology University of Reading
Global warming and Extremes of Weather Prof. Richard Allan, Department of Meteorology University of Reading Extreme weather climate change Recent extreme weather focusses debate on climate change Can we
More informationNo pause in the increase of hot temperature extremes
SUPPLEMENTARY INFORMATION DOI: 10.1038/NCLIMATE2145 No pause in the increase of hot temperature extremes Sonia I. Seneviratne 1, Markus G. Donat 2,3, Brigitte Mueller 4,1, and Lisa V. Alexander 2,3 1 Institute
More informationLarge-scale changes in the atmospheric water cycle in models and observations
Large-scale changes in the atmospheric water cycle in models and observations Richard Allan University of Reading Strength of Feedback (Wm-2/oC) Uncertainty in strength of cloud feedback Total range Range
More informationPotential impact of initialization on decadal predictions as assessed for CMIP5 models
GEOPHYSICAL RESEARCH LETTERS, VOL. 39,, doi:10.1029/2012gl051974, 2012 Potential impact of initialization on decadal predictions as assessed for CMIP5 models Grant Branstator 1 and Haiyan Teng 1 Received
More informationChanges in the distribution of rain frequency and intensity in response to global warming
Changes in the distribution of rain frequency and intensity in response to global warming Angeline G. Pendergrass 1 and Dennis L. Hartmann Department of Atmospheric Sciences, University of Washington,
More informationA Flexible Climate Model For Use In Integrated Assessments
A Flexible Climate Model For Use In Integrated Assessments Andrei P. Sokolov and Peter H. Stone Center for Global Change Science. Massachusetts Institute of Technology, 77 Massachusetts Ave. Room 54-1312,
More information