Change in climate variability in the 21st century

As climate changes due to the increase of greenhouse gases, there is the potential for climate variability to change as well. The change in variability of temperature and precipitation in a transient climate simulation, where trace gases are allowed to increase gradually, and in the doubled CO₂ climate is investigated using the GISS general circulation model. The current climate control run is compared with observations and with the climate change simulations for variability on three time-scales: interannual variability, daily variability, and the amplitude of the diurnal cycle. The results show that the modeled variability is often larger than observed, especially in late summer, possibly due to the crude ground hydrology. In the warmer climates, temperature variability and the diurnal cycle amplitude usually decrease, in conjunction with a decrease in the latitudinal temperature gradient and the increased greenhouse inhibition of radiative cooling. Precipitation variability generally changes with the same sign as the mean precipitation itself, usually increasing in the warmer climate. Changes at a particular grid box are often not significant, with the prevailing tendency determined from a broader sampling. Little change is seen in daily persistence. The results are relevant to the continuing assessments of climate change impacts on society, though their use should be tempered by appreciation of the model deficiencies for the current climate.     

Impact of vegetation feedback on the temperature and its diurnal range over the Northern Hemisphere during summer in a 2 × CO2 climate

This study examines the potential impact of vegetation feedback on the changes in the diurnal temperature range (DTR) due to the doubling of atmospheric CO₂ concentrations during summer over the Northern Hemisphere using a global climate model equipped with a dynamic vegetation model. Results show that CO₂ doubling induces significant increases in the daily mean temperature and decreases in DTR regardless of the presence of the vegetation feedback effect. In the presence of vegetation feedback, increase in vegetation productivity related to warm and humid climate lead to (1) an increase in vegetation greenness in the mid-latitude and (2) a greening and the expansion of grasslands and boreal forests into the tundra region in the high latitudes. The greening via vegetation feedback induces contrasting effects on the temperature fields between the mid- and high-latitude regions. In the mid-latitudes, the greening further limits the increase in T _max more than T _min, resulting in further decreases in DTR because the greening amplifies evapotranspiration and thus cools daytime temperature. The greening in high-latitudes, however, it reinforces the warming by increasing T _max more than T _min to result in a further increase in DTR from the values obtained without vegetation feedback. This effect on T _max and DTR in the high latitude is mainly attributed to the reduction in surface albedo and the subsequent increase in the absorbed insolation. Present study indicates that vegetation feedback can alter the response of the temperature field to increases in CO₂ mainly by affecting the T _max and that its effect varies with the regional climate characteristics as a function of latitudes.     

Spatial-temporal controls on Cooling Degree Hours: An energy demand parameter

Summary Present-day and 2 × CO₂ regional climate impacts and the effects of local land use patterns on Cooling Degree Hours (CDHs — an energy demand parameter based on cumulative degrees of temperature above 75 °F) are investigated for the Phoenix metropolitan area in central Arizona. Approaches include: (1) the utilization of four readily available and commonly used Global Circulation Models (GCMs) to assess possible changes in climate for doubled CO₂, (2) the analysis of hourly temperature data collected for one year over three different land type sites, and (3) analysis of locally collected hourly temperature data, for a typical summer day from a real-time weather and climate network, to evaluate the spatial variability of CDHs over the urban landscape. Results are discussed by showing effects at the global and urban scales. Differing surface types, and expected changes in land uses in the future, induce spatial differences of CDHs (and therefore potential energy demand) comparable to GCM projections of climate change for the region.With 6 Figures     

Present and future surface climate in the western USA as simulated by 15 global climate models

We analyze results of 15 global climate simulations contributed to the Coupled Model Intercomparison Project (CMIP). Focusing on the western USA, we consider both present climate simulations and predicted responses to increasing atmospheric CO₂. The models vary in their ability to predict the present climate. In the western USA, a few models produce a seasonal cycle for spatially averaged temperature and/or precipitation in good agreement with observational data. Other models tend to over-predict precipitation in the winter or exaggerate the amplitude of the seasonal cycle of temperature. The models also differ in their ability to reproduce the spatial patterns of temperature and precipitation in the USA. Considering the monthly mean precipitation responses to doubled atmospheric CO₂, averaged over the western USA, we find some models predict increases while others predict decreases. The predicted temperature response, on the other hand, is invariably positive over this region; however, for each month, the range of values given by the different models is large compared to the mean model response. We look for possible relationships between the models temperature and precipitation responses to doubled CO₂ concentration and their ability to simulate some aspects of the present climate. We find that these relationships are weak, at best. The precipitation response over the western USA in DJF and the precipitation response over the mid- and tropical latitudes seem to be correlated with the RMS error in simulated present-day precipitation, also calculated over the mid- and tropical latitudes. However, considering only the responses of the models with the smallest RMS errors does not provide a different estimate of the precipitation response to a doubled CO₂ concentration, because even among the most accurate models, the range of model responses is so large. For temperature, we find that models that have smaller RMS errors in present-climate temperature in the north eastern Pacific region predict a higher temperature response in the western USA than the models with larger errors. A similar relation exists between the temperature response over Europe in DJF and the RMS error calculated over the Northern Atlantic.     

Annual and seasonal climate and climatic changes in the Canadian prairies simulated by the CCC GCM

Abstract

As part of a study on the effects of climatic variability and change on the sustainability of agriculture in Alberto, the modelling performance of the second‐generation Canadian Climate Centre GCM (general circulation model) is examined. For the region in general, the simulation of 1 × CO₂ mean temperature is generally better than that for mean precipitation, and summer is the season best modelled for each variable. At the scale of individual grid squares, DJF (December, January, February) (temperature) and JJA (June, July, August) (precipitation) are the seasons best modelled. The GCM‐simulated increases in mean annual temperature resulting from a doubling of CO₂ are of the order of 5 to 6°C in the Prairie region, with much of this increase resulting from substantial warming in the winter and spring. Increases in mean annual precipitation are of the order of 50 to 150 mm (changes of +5 to +15%), with the greatest changes again occurring in winter and spring. As far as the limited GCM run durations allow, temperature and precipitation variance generally show no significant changes from a 1 × CO₂ to a 2 × CO₂ climate. Increased precipitation in winter and spring does not result in greater snow accumulations owing to the magnitude of warming; and significant decreases in soil moisture content occur in summer and fall. The resulting effects on the growing season and moisture regime have the potential to affect agricultural practices in the area.     

Vulnerability and adaptation assessments of agriculturalcrops under climate change in the Southeastern USA

Summary  It is expected that a change in climatic conditions due to global warming will directly impact agricultural production. Most climate change studies have been applied at very large scales, in which regions were represented by only one or two weather stations, which were mainly located at airports of major cities. The objective of this study was to determine the potential impact of climate change at a local level, taking into account weather data recorded at remote locations. Daily weather data for a 30-year period were obtained for more than 500 sites, representing the southeastern region of the USA. Climate change scenarios, using transient and equilibrium global circulation models (GCM), were defined, created and applied to the daily historical weather data. The modified temperature, precipitation and solar radiation databases corresponding to each of the climate change scenarios were used to run the CERES v.3.5 simulation model for maize and winter wheat and the CROPGRO v.3.5 model for soybean and peanut. The GCM scenarios projected a shorter duration of the crop-growing season. Under the current level of CO₂, the GCM scenarios projected a decrease of crop yields in the 2020s. When the direct effects of CO₂ were assumed in the study, the scenarios resulted in an increase in soybean and peanut yield. Under equilibrium , the GCM climate change scenarios projected a decrease of maize and winter wheat yield. The indirect effects of climate change also tended to decrease soybean and peanut yield. However, when the direct effects of CO₂ were included, most of the scenarios resulted in an increase in legume yields. Possible changes in sowing data, hybrids and cultivar selection, and fertilization were considered as adaptation options to mitigate the potential negative impact of potential warming. Received July 20, 1999/Revised April 18, 2000     

Quantifying uncertainty in changes in extreme event frequency in response to doubled CO2 using a large ensemble of GCM simulations

We discuss equilibrium changes in daily extreme surface air temperature and precipitation events in response to doubled atmospheric CO₂, simulated in an ensemble of 53 versions of HadSM3, consisting of the HadAM3 atmospheric general circulation model (GCM) coupled to a mixed layer ocean. By virtue of its size and design, the ensemble, which samples uncertainty arising from the parameterisation of atmospheric physical processes and the effects of natural variability, provides a first opportunity to quantify the robustness of predictions of changes in extremes obtained from GCM simulations. Changes in extremes are quantified by calculating the frequency of exceedance of a fixed threshold in the 2 × CO₂ simulation relative to the 1 × CO₂ simulation. The ensemble-mean value of this relative frequency provides a best estimate of the expected change while the range of values across the ensemble provides a measure of the associated uncertainty. For example, when the extreme threshold is defined as the 99th percentile of the 1 × CO₂ distribution, the global-mean ensemble-mean relative frequency of extremely warm days is found to be 20 in January, and 28 in July, implying that events occurring on one day per hundred under present day conditions would typically occur on 20–30 days per hundred under 2 × CO₂ conditons. However the ensemble range in the relative frequency is of similar magnitude to the ensemble-mean value, indicating considerable uncertainty in the magnitude of the increase. The relative frequencies in response to doubled CO₂ become smaller as the threshold used to define the extreme event is reduced. For one variable (July maximum daily temperature) we investigate this simulated variation with threshold, showing that it can be quite well reproduced by assuming the response to doubling CO₂ to be characterised simply as a uniform shift of a Gaussian distribution. Nevertheless, doubling CO₂ does lead to changes in the shape of the daily distributions for both temperature and precipitation, but the effect of these changes on the relative frequency of extreme events is generally larger for precipitation. For example, around one-fifth of the globe exhibits ensemble-mean decreases in time-averaged precipitation accompanied by increases in the frequency of extremely wet days. The ensemble range of changes in precipitation extremes (relative to the ensemble mean of the changes) is typically larger than for temperature extremes, indicating greater uncertainty in the precipitation changes. In the global average, extremely wet days are predicted to become twice as common under 2 × CO₂ conditions. We also consider changes in extreme seasons, finding that simulated increases in the frequency of extremely warm or wet seasons under 2 × CO₂ are almost everywhere greater than the corresponding increase in daily extremes. The smaller increases in the frequency of daily extremes is explained by the influence of day-to-day weather variability which inflates the variance of daily distributions compared to their seasonal counterparts.     

The impact of climate change on continuous corn production in the Southern U.S.A.

The Goddard Institute for Space Studies (GISS) General Circulation Model (GCM) has been used in conjunction with a field level plant process model (CERES-Maize) and a field level pesticide transport model (PRZM) to study the impacts of doubled levels of atmospheric CO₂ on various aspects of corn production in the Southern U.S.A. Grid-box scale GCM output has been applied to a 38-year time series of historical weather data at 28 different locations for several typical soil profiles throughout the South. Limitations on the use of the climate scenario in conjunction with the process models are discussed. Major shortcomings include: 1) no direct impacts of atmospheric CO₂ on plant growth and development in the plant process model; 2) neither macro-pore solute transport nor chemical decay rate response to temperature are included in the pesticide transport model; and 3) the climate change scenario output does not provide information concerning changes in temperature extremes and variability or precipitation frequency, intensity or duration. The latter are particularly critical parameters for the detailed simulation of hydrological processes. In spite of these omissions, the combination of the three models facilitates the study of the impacts of GCM modeled climate change on several inter-related agro-climatic issues of interest to agricultural policy makers. These issues include: changes in dryland and irrigated corn yields; changes in sowing and harvest dates; modification of crop water demand; and estimates of effects on pesticide losses from the soil surface and through leaching from the bottom of the active corn root zone. Model generated results which address these issues are presented but must be used with caution in light of the GCM and process model limitations. The results of this study suggest that substantial changes in agricultural production and management practices may be needed to respond to the climate changes expected to take place throughout the Southern U.S.A.     

PROJECTIONS OF CLIMATE CHANGE FOR THE CZECH REPUBLIC

The paper deals with a selection of the climatological baseline, GCM validity and construction of the climate change scenarios for an impact assessment in the Czech territory. The period of 1961–1990 has been selected as the climatological baseline. The corresponding database includes more than 50 monthly mean temperature and precipitation series, and 16 time series of daily meteorological data that contain also the solar radiation data. The 1× CO₂ outputs produced by four GCMs, provided by the CSMT (GISS, GFD30, GFD01, and CCCM), were compared with observed temperature and precipitation conditions in western and central Europe with a particular attention devoted to the Czech territory. The GCM ability to simulate annual cycles of temperature, precipitation and radiation was thoroughly examined. The GISS and CCCM were selected as a basis for constructing climate change scenarios as they simulated reasonably the observed patterns. According to the GISS variant, 2× CO₂ climate assumes a higher winter and lower summer warming, and an increase in annual precipitation amounts. A dangerous combination of the summer temperature increase and declining precipitation amounts is a specific feature of the CCCM scenario. An incremental scenario for temperature and precipitation is based on the combination of prescribed changes in both annual means and annual courses.     

MEAN AND VARIANCE CHANGE IN CLIMATE SCENARIOS: METHODS, AGRICULTURAL APPLICATIONS, AND MEASURES OF UNCERTAINTY

Our central goal is to determine the importance of including both mean and variability changes in climate change scenarios in an agricultural context. By adapting and applying a stochastic weather generator, we first tested the sensitivity of the CERES-Wheat model to combinations of mean and variability changes of temperature and precipitation for two locations in Kansas. With a 2°C increase in temperature with daily (and interannual) variance doubled, yields were further reduced compared to the mean only change. In contrast, the negative effects of the mean temperature increase were greatly ameliorated by variance decreased by one-half. Changes for precipitation are more complex, since change in variability naturally attends change in mean, and constraining the stochastic generator to mean change only is highly artificial. The crop model is sensitive to precipitation variance increases with increased mean and variance decreases with decreased mean. With increased mean precipitation and a further increase in variability Topeka (where wheat cropping is not very moisture limited) experiences decrease in yield after an initial increase from the 'mean change only case. At Goodland Kansas, a moisture-limited site where summer fallowing is practiced, yields are decreased with decreased precipitation, but are further decreased when variability is further reduced. The range of mean and variability changes to which the crop model is sensitive are within the range of changes found in regional climate modeling (RegCM) experiments for a CO₂ doubling (compared to a control run experiment). We then formed two types of climate change scenarios based on the changes in climate found in the control and doubled CO₂ experiments over the conterminous U. S. of RegCM: (1) one using only mean monthly changes in temperature, precipitation, and solar radiation; and (2) another that included these mean changes plus changes in daily (and interannual) variability. The scenarios were then applied to the CERES-Wheat model at four locations (Goodland, Topeka, Des Moines, Spokane) in the United States. Contrasting model responses to the two scenarios were found at three of the four sites. At Goodland, and Des Moines mean climate change increased mean yields and decreased yield variability, but the mean plus variance climate change reduced yields to levels closer to their base (unchanged) condition. At Spokane mean climate change increased yields, which were somewhat further increased with climate variability change. Three key aspects that contribute to crop response are identified: the marginality of the current climate for crop growth, the relative size of the mean and variance changes, and timing of these changes. Indices for quantifying uncertainty in the impact assessment were developed based on the nature of the climate scenario formed, and the magnitude of difference between model and observed values of relevant climate variables.     

区域尺度土地利用/土地覆盖变化对气温日较差的影响

利用NCAR CAM4.0模式,针对潜在植被和当代植被两种典型土地覆盖类型,通过平衡态试验探讨土地利用/土地覆盖变化（land use/land cover change, LUCC）对气候的影响。结果表明,LUCC对气温日较差有明显影响。日较差的响应与LUCC变化的区域有紧密的联系。在中纬度,LUCC引起日较差减小,这主要由日最高气温的降低造成。在低纬度东亚地区,日较差的减小主要由日最高气温的降低造成;而在印度半岛,日较差的减小主要由日最低气温的升高决定。这种区域性的差异,主要是由于植被蒸腾和冠层蒸发的作用,LUCC能够显著调节气温日较差的变化。     

Modeling the potential effects of climate change on water temperature downstream of a shallow reservoir,lower madison river,MT

A numerical stream temperature model that accounts for kinematic wave flow routing, and heat exchange fluxes between stream water and the atmosphere, and stream water and the stream bed is developed and calibrated to a data-set from the Lower Madison River, Montana, USA. Future climate scenarios were applied to the model through changes to the atmospheric input data based on air temperature and solar radiation output from four General Circulation Models (GCM) for the region under atmospheric CO₂ concentration doubling. The purpose of this study was to quantify potential climate change impacts on water temperature for the Lower Madison River, and to assess possible impacts to aquatic ecosystems. Because water temperature is a critical component of fish habitat, this information could be of use in future planning operations of current reservoirs. We applied air temperature changes to diurnal temperatures, daytime temperatures only, and nighttime temperatures only, to assess the impacts of variable potential warming trends. The results suggest that, given the potential climatic changes, the aquatic ecosystem downstream of Ennis Lake will experience higher water temperatures, possibly leading to increased stress on fish populations.Daytime warming produced the largest increases in downstream water temperature.     

Use of general circulation model output in the creation of climate change scenarios for impact analysis

Many scientific studies warn of a rapid global climate change during the next century. These changes are understood with much less certainty on a regional scale than on a global scale, but effects on ecosystems and society will occur at local and regional scales. Consequently, in order to study the true impacts of climate change, regional scenarios of future climate are needed. One of the most important sources of information for creating scenarios is the output from general circulation models (GCMs) of the climate system. However, current state-of-the-art GCMs are unable to simulate accurately even the current seasonal cycle of climate on a regional basis. Thus the simple technique of adding the difference between 2 × CO₂ and 1 × CO₂ GCM simulations to current climatic time series cannot produce scenarios with appropriate spatial and temporal details without corrections for model deficiencies. In this study a technique is developed to allow the information from GCM simulations to be used, while accommodating for the deficiencies. GCM output is combined with knowledge of the regional climate to produce scenarios of the equilibrium climate response to a doubling of the atmospheric CO₂ concentration for three case study regions, China, Sub-Saharan Africa and Venezuela, for use in biological effects models. By combining the general climate change calculated with several GCMs with the observed patterns of interannual climate variability, reasonable scenarios of temperature and precipitation variations can be created. Generalizations of this procedure to other regions of the world are discussed.     

Local temperature estimation under climate change

Summary A methodology to estimate the space-time distribution of daily mean temperature under climate change is developed and applied to a central Nebraska case study. The approach is based on the analysis of the Markov properties of atmospheric circulation pattern (CP) types, and a stochastic linkage between daily (here 500hPa) CP types and daily mean temperatures. Historical data and general circulation model (GCM) output of daily CP corresponding to 1 × CO₂ and 2 × CO₂ scenarios are considered. The relationship between spatially averaged geopotential height of the 500 hPa surface — within each CP type — and daily mean temperature is described by a nonparametric regression technique. Time series of daily mean temperatures corresponding to each of these cases are simulated and their statistical properties are compared. Under the climate of central Nebraska, the space-time response of daily mean temperature to global climate change is variable. In general, a warmer climate appears to cause about 5°C increase in the winter months, a smaller increase in other months with no change in July and August. The sensitivity of the results to the GCM utilized should be considered.On leave from the Department of Meteorology, Eötvós Loránd University, Budapest, Hungary.With 14 Figures     

Great Lakes Hydrology Under Transposed Climates

Historical climates, based on 43 years of daily data from areas south and southwest of the Great Lakes, were used to examine the hydrological response of the Great Lakes to warmer climates. The Great Lakes Environmental Research Laboratory used their conceptual models for simulating moisture storages in, and runoff from, the 121 watersheds draining into the Great Lakes, over-lake precipitation into each lake, and the heat storages in, and evaporation from, each lake. This transposition of actual climates incorporates natural changes in variability and timing within the existing climate; this is not true for General Circulation Model-generated corrections applied to existing historical data in many other impact studies. The transposed climates lead to higher and more variable over-land evapotranspiration and lower soil moisture and runoff with earlier runoff peaks since the snow pack is reduced up to 100%. Water temperatures increase and peak earlier. Heat resident in the deep lakes increases throughout the year. Buoyancy-driven water column turnover frequency drops and lake evaporation increases and spreads more throughout the annual cycle. The response of runoff to temperature and precipitation changes is coherent among the lakes and varies quasi-linearly over a wide range of temperature changes, some well beyond the range of current GCM predictions for doubled CO₂ conditions.     

Potential CO2-induced climate effects on North American wheat-producing regions

The environmental requirements for growth of winter, spring, and fallsown spring wheats in North America are specified and compared to temperature results from the control run of the Goddard Institute for Space Studies general circulation model (GISS GCM) and observed precipitation in order to generate a simulated map of current wheat production regions. The simulation agrees substantially with the actual map of wheat-growing regions in North America. Results from a doubled CO₂ run of the climate model are then used to generate wheat regions under the new climatic conditions. In the simulation, areas of production increase in North America, particularly in Canada, due to increased growing degree units (GDU). Although wheat classifications may change, major wheat regions in the United States remain the same under simulated doubled CO₂ conditions. The wheat-growing region of Mexico is identified as vulnerable due to high temperature stress. Higher mean temperatures during wheat growth, particularly during the reproductive stages, may increase the need for earlier-maturing, more heat-tolerant cultivars throughout North, America. The soil moisture diagnostic of the climate model is used to analyze potential water availability in the major wheat region of the Southern Great Plains.     

Simulated Climate Change Effects on Year-Round Water Temperatures in Temperate Zone Lakes

A deterministic, one-dimensional model is presented to simulate daily water temperature profiles and associated ice and snow covers for dimictic and polymictic lakes of the temperate zone. The lake parameters required as model input are surface area (A_s), maximum depth (H_MAX), and Secchi depth (z_s), the latter, used as a measure of light attenuation and trophic state. The model is driven by daily weather data and operates year-round over multiple years. The model has been tested with extensive data (over 5,000 temperature points). Standard error between simulated and measured water temperatures is 1.4°C in the open water season and 0.5°C in the ice cover season. The model is applied to simulate the sensitivity of Minnesota lake water temperature characteristics to climate change. The projected climate changes due to a doubling of atmospheric CO₂ are obtained from the output of the Canadian Climate Center General Circulation Model (CCC GCM) and the Goddard Institute of Space Studies General Circulation Model (GISS GCM). Simulated lake temperature characteristics have been plotted in a coordinate system with a lake geometry ratio (A _s ^0.25 /H_MAX) on one axis and Secchi depth on the other. The lake geometry ratio expresses a lake's susceptibility to stratification. By interpolation, the sensitivity of lake temperature characteristics to changes of water depth and Secchi depth under the projected climate scenarios can therefore be obtained. Selected lake temperature characteristics simulated with past climate conditions (1961–1979) and with a projected 2 × CO₂ climate scenario as input are presented herein in graphical form. The simulation results show that under the 2 × CO₂ climate scenario ice formation is delayed and ice cover period is shortened. These changes cause water temperature modifications throughout the year.     

VARIABILITY OF DIURNAL TEMPERATURE RANGE IN EAST AFRICA DURING 1921–2010

This study investigates the long-term spatiotemporal variability of diurnal temperature range(DTR) in East Africa(EA). The study carries out non-parametric trend analysis of gridded DTR monthly data sourced from Climatic Research Unit(CRU). The DTR exhibits mixed signals in space and time over EA. The DTR correlates negatively with rainfall over EA. Reduction in DTR coincides with the summer season in the northern and southern hemispheres respectively, suggesting the influence of cloud cover on it. There was a non-uniform pattern of DTR changes across the region with time. Lake Victoria basin recorded the highest warming rates. The Indian Ocean coast recorded the least spatiotemporal variability in DTR. A reduction in DTR is evident in the two seasons: hot and cold. The start of the study period; 1921—1930, was the coolest decade in the study period. Most parts of EA recorded negative DTR anomalies in 1961—1970. The overall reduction in DTR throughout the study period highlights the ongoing warming which is a global phenomenon. There remains need for investigating the causation of the observed DTR variability for effective monitoring of the variability in future.     

An examination of simulated regional climate change using an adjoint method

The adjoint of a one-layer model of tropospheric-average temperature advection is used to examine a general circulation model (GCM) doubled CO₂ scenario experiment locally over Europe. The adjoint technique enables a regional temperature anomaly to be accounted for in terms of horizontal advection and thermodynamic sources and sinks, both local and remote. Although the time-averaged regional signal in tropospheric-average temperature over Central Europe in the doubled CO₂ GCM experiment is very small ( 0.1 K) once the Northern Hemispheric mean (+2.2 K) has been subtracted, there is a large variability on decadal time scales, and it is toward one such event that we direct our attention. It is found that a 10-January-mean regional anomaly (2×CO₂-Control) of –1.7 K (with respect to hemispheric average) is primarily accounted for by changes in the advecting winds. The main thermodynamic forcing anomalies during January are situated over Europe itself and upstream over the Atlantic, but these are found to have a secondary direct effect, although their indirect effect via changes in the flow pattern remains to be determined.     

Quantifying uncertainties in projections of extremes��a perturbed land surface parameter experiment

Uncertainties in the climate response to a doubling of atmospheric CO₂ concentrations are quantified in a perturbed land surface parameter experiment. The ensemble of 108 members is constructed by systematically perturbing five poorly constrained land surface parameters of global climate model individually and in all possible combinations. The land surface parameters induce small uncertainties at global scale, substantial uncertainties at regional and seasonal scale and very large uncertainties in the tails of the distribution, the climate extremes. Climate sensitivity varies across the ensemble mainly due to the perturbation of the snow albedo parameterization, which controls the snow albedo feedback strength. The uncertainty range in the global response is small relative to perturbed physics experiments focusing on atmospheric parameters. However, land surface parameters are revealed to control the response not only of the mean but also of the variability of temperature. Major uncertainties are identified in the response of climate extremes to a doubling of CO₂. During winter the response both of temperature mean and daily variability relates to fractional snow cover. Cold extremes over high latitudes warm disproportionately in ensemble members with strong snow albedo feedback and large snow cover reduction. Reduced snow cover leads to more winter warming and stronger variability decrease. As a result uncertainties in mean and variability response line up, with some members showing weak and others very strong warming of the cold tail of the distribution, depending on the snow albedo parametrization. The uncertainty across the ensemble regionally exceeds the CMIP3 multi-model range. Regarding summer hot extremes, the uncertainties are larger than for mean summer warming but smaller than in multi-model experiments. The summer precipitation response to a doubling of CO₂ is not robust over many regions. Land surface parameter perturbations and natural variability alter the sign of the response even over subtropical regions.