How weather impacts the forced climate response

The new interactive ensemble modeling strategy is used to diagnose how noise due to internal atmospheric dynamics impacts the forced climate response during the twentieth century (i.e., 1870?C1999). The interactive ensemble uses multiple realizations of the atmospheric component model coupled to a single realization of the land, ocean and ice component models in order to reduce the noise due to internal atmospheric dynamics in the flux exchange at the interface of the component models. A control ensemble of so-called climate of the twentieth century simulations of the Community Climate Simulation Model version 3 (CCSM3) are compared with a similar simulation with the interactive ensemble version of CCSM3. Despite substantial differences in the overall mean climate, the global mean trends in surface temperature, 500?mb geopotential and precipitation are largely indistinguishable between the control ensemble and the interactive ensemble. Large differences in the forced response; however, are detected particularly in the surface temperature of the North Atlantic. Associated with the forced North Atlantic surface temperature differences are local differences in the forced precipitation and a substantial remote rainfall response in the deep tropical Pacific. We also introduce a simple variance analysis to separately compare the variance due to noise and the forced response. We find that the noise variance is decreased when external forcing is included. In terms of the forced variance, we find that the interactive ensemble increases this variance relative to the control.     

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.     

Influence of SST biases on future climate change projections

We use a quantile-based bias correction technique and a multi-member ensemble of the atmospheric component of NCAR CCSM3 (CAM3) simulations to investigate the influence of sea surface temperature (SST) biases on future climate change projections. The simulations, which cover 1977?C1999 in the historical period and 2077?C2099 in the future (A1B) period, use the CCSM3-generated SSTs as prescribed boundary conditions. Bias correction is applied to the monthly time-series of SSTs so that the simulated changes in SST mean and variability are preserved. Our comparison of CAM3 simulations with and without SST correction shows that the SST biases affect the precipitation distribution in CAM3 over many regions by introducing errors in atmospheric moisture content and upper-level (lower-level) divergence (convergence). Also, bias correction leads to significantly different precipitation and surface temperature changes over many oceanic and terrestrial regions (predominantly in the tropics) in response to the future anthropogenic increases in greenhouse forcing. The differences in the precipitation response from SST bias correction occur both in the mean and the percent change, and are independent of the ocean?Catmosphere coupling. Many of these differences are comparable to or larger than the spread of future precipitation changes across the CMIP3 ensemble. Such biases can affect the simulated terrestrial feedbacks and thermohaline circulations in coupled climate model integrations through changes in the hydrological cycle and ocean salinity. Moreover, biases in CCSM3-generated SSTs are generally similar to the biases in CMIP3 ensemble mean SSTs, suggesting that other GCMs may display a similar sensitivity of projected climate change to SST errors. These results help to quantify the influence of climate model biases on the simulated climate change, and therefore should inform the effort to further develop approaches for reliable climate change projection.     

Using climate impacts indicators to evaluate climate model ensembles: temperature suitability of premium winegrape cultivation in the United States

We explore the potential to improve understanding of the climate system by directly targeting climate model analyses at specific indicators of climate change impact. Using the temperature suitability of premium winegrape cultivation as a climate impacts indicator, we quantify the inter- and intra-ensemble spread in three climate model ensembles: a physically uniform multi-member ensemble consisting of the RegCM3 high-resolution climate model nested within the NCAR CCSM3 global climate model; the multi-model NARCCAP ensemble consisting of single realizations of multiple high-resolution climate models nested within multiple global climate models; and the multi-model CMIP3 ensemble consisting of realizations of multiple global climate models. We find that the temperature suitability for premium winegrape cultivation is substantially reduced throughout the high-value growing areas of California and the Columbia Valley region (eastern Oregon and Washington) in all three ensembles in response to changes in temperature projected for the mid-twenty first century period. The reductions in temperature suitability are driven primarily by projected increases in mean growing season temperature and occurrence of growing season severe hot days. The intra-ensemble spread in the simulated climate change impact is smaller in the single-model ensemble than in the multi-model ensembles, suggesting that the uncertainty arising from internal climate system variability is smaller than the uncertainty arising from climate model formulation. In addition, the intra-ensemble spread is similar in the NARCCAP nested climate model ensemble and the CMIP3 global climate model ensemble, suggesting that the uncertainty arising from the model formulation of fine-scale climate processes is not smaller than the uncertainty arising from the formulation of large-scale climate processes. Correction of climate model biases substantially reduces both the inter- and intra-ensemble spread in projected climate change impact, particularly for the multi-model ensembles, suggesting that—at least for some systems—the projected impacts of climate change could be more robust than the projected climate change. Extension of this impacts-based analysis to a larger suite of impacts indicators will deepen our understanding of future climate change uncertainty by focusing on the climate phenomena that most directly influence natural and human systems.     

Variability and predictability of Northeast China climate during 1948–2012

In this work, authors examine the variabilities of precipitation and surface air temperature (T2m) in Northeast China during 1948–2012, and their global connection, as well as the predictability. It is noted that both the precipitation and T2m variations in Northeast China are dominated by interannual and higher frequency variations. However, on interdecadal time scales, T2m is shifted significantly from below normal to above normal around 1987/1988. Statistically, the seasonal mean precipitation and T2m are largely driven by local internal atmospheric variability rather than remote forcing. For the precipitation variation, circulation anomalies in the low latitudes play a more important role in spring and summer than in autumn and winter. For T2m variations, the associated sea surface pressure (SLP) and 850-hPa wind (uv850) anomalies are similar for all seasons in high latitudes with significantly negative correlations for SLP and westerly wind anomaly for uv850, suggesting that a strong zonal circulation in the high latitudes favors warming in Northeast China. The predictability of precipitation and T2m in Northeast China is assessed by using the Atmospheric Model Inter-comparison Project type experiments which are forced by observed sea surface temperature (SST) and time-evolving greenhouse gas (GHG) concentrations. Results suggest that T2m has slightly higher predictability than precipitation in Northeast China. To some extent, the model simulates the interdecadal shift of T2m around 1987/1988, implying a possible connection between SST (and/or GHG forcing) and surface air temperature variation in Northeast China on interdecadal time scales. Nevertheless, the precipitation and T2m variations are mainly determined by the unpredictable components which are caused by the atmospheric internal dynamic processes, suggesting low predictability for the climate variation in Northeast China.     

Predictable and Unpredictable Components of the Summer East Asia–Pacific Teleconnection Pattern

The East Asia–Pacific(EAP) teleconnection pattern is the dominant mode of circulation variability during boreal summer over the western North Pacific and East Asia, extending from the tropics to high latitudes. However, much of this pattern is absent in multi-model ensemble mean forecasts, characterized by very weak circulation anomalies in the mid and high latitudes. This study focuses on the absence of the EAP pattern in the extratropics, using state-of-the-art coupled seasonal forecast systems. The results indicate that the extratropical circulation is much less predictable, and lies in the large spread among different ensemble members, implying a large contribution from atmospheric internal variability. However,the tropical–mid-latitude teleconnections are also relatively weaker in models than observations, which also contributes to the failure of prediction of the extratropical circulation. Further results indicate that the extratropical EAP pattern varies closely with the anomalous surface temperatures in eastern Russia, which also show low predictability. This unpredictable circulation–surface temperature connection associated with the EAP pattern can also modulate the East Asian rainband.     

An overview of decadal climate predictability in a multi-model ensemble by climate model MIROC

Decadal climate predictability is examined in hindcast experiments by a multi-model ensemble using three versions of the coupled atmosphere-ocean model MIROC. In these hindcast experiments, initial conditions are obtained from an anomaly assimilation procedure using the observed oceanic temperature and salinity with prescribed natural and anthropogenic forcings on the basis of the historical data and future emission scenarios in the Intergovernmental Panel of Climate Change. Results of the multi-model ensemble in our hindcast experiments show that predictability of surface air temperature (SAT) anomalies on decadal timescales mostly originates from externally forced variability. Although the predictable component of internally generated variability has considerably smaller SAT variance than that of externally forced variability, ocean subsurface temperature variability has predictive skills over almost a decade, particularly in the North Pacific and the North Atlantic where dominant signals associated with Pacific decadal oscillation (PDO) and the Atlantic multidecadal oscillation (AMO) are observed. Initialization enhances the predictive skills of AMO and PDO indices and slightly improves those of global mean temperature anomalies. Improvement of these predictive skills in the multi-model ensemble is higher than that in a single-model ensemble.     

Twentieth century north atlantic climate change. Part I: assessing determinism

Boreal winter North Atlantic climate change since 1950 is well described by a trend in the leading spatial structure of variability, known as the North Atlantic Oscillation (NAO). Through diagnoses of ensembles of atmospheric general circulation model (AGCM) experiments, we demonstrate that this climate change is a response to the temporal history of sea surface temperatures (SSTs). Specifically, 58 of 67 multi-model ensemble members (87%), forced with observed global SSTs since 1950, simulate a positive trend in a winter index of the NAO, and the spatial pattern of the multi-model ensemble mean trend agrees with that observed. An ensemble of AGCM simulations with only tropical SST forcing further suggests that variations in these SSTs are of primary importance. The probability distribution function (PDF) of 50-year NAO index trends from the forced simulations are, moreover, appreciably different from the PDF of a control simulation with no interannual SST variability, although chaotic atmospheric variations are shown to yield substantial 50-year trends. Our results thus advance the view that the observed linear trend in the winter NAO index is a combination of a strong tropically forced signal and an appreciable noise component of the same phase. The changes in tropical rainfall of greatest relevance include increased rainfall over the equatorial Indian Ocean, a change that has likely occurred in nature and is physically consistent with the observed, significant warming trend of the underlying sea surface.     

Assessment of the Capability of ENSEMBLES Hindcasts in Predicting Spring Climate in China

Using the hindcasts provided by the Ensemble-Based Predictions of Climate Changes and Their Impacts(ENSEMBLES) project for the period of 1980–2005, the forecast capability of spring climate in China is assessed mainly from the aspects of precipitation, 2-m air temperature, and atmospheric circulations. The ENSEMBELS can reproduce the climatology and dominant empirical orthogonal function(EOF) modes of precipitation and 2-m air temperature, with some differences arising from different initialization months. The multi-model ensemble(MME) forecast of interannual variability is of good performance in some regions such as eastern China with February initialization.The spatial patterns of the MME interannual and inter-member spreads for precipitation and 2-m air temperature are consistent with those of the observed interannual spread, indicating that internal dynamic processes have major impacts on the interannual anomaly of spring climate in China. We have identified two coupled modes between intermember anomalies of the 850-hPa vorticity in spring and sea surface temperature(SST) both in spring and at a lead of 2 months, of which the first mode shows a significant impact on the spring climate in China, with an anomalous anticyclone located over Northwest Pacific and positive precipitation and southwesterly anomalies in eastern China.Our results also suggest that the SST at a lead of two months may be a predictor for the spring climate in eastern China. A better representation of the ocean–atmosphere interaction over the tropical Pacific, Northwest Pacific, and Indian Ocean can improve the forecast skill of the spring climate in eastern China.     

Vegetation feedback under future global warming

It has been well documented that vegetation plays an important role in the climate system. However, vegetation is typically kept constant when climate models are used to project anthropogenic climate change under a range of emission scenarios in the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emission Scenarios. Here, an atmospheric general circulation model, and an asynchronously coupled system of an atmospheric and an equilibrium terrestrial biosphere model are forced by monthly sea surface temperature and sea ice extent for the periods 2051?C2060 and 2090?C2098 as projected with 17 atmosphere?Cocean general circulation models participating in the IPCC Fourth Assessment Report, and by appropriate atmospheric carbon dioxide concentrations under the A2 emission scenario. The effects of vegetation feedback under future global warming are then investigated. It is found that the simulated composition and distribution of vegetation during 2051?C2060 (2090?C2098) differ greatly from the present, and global vegetation tends to become denser as expressed by a 21% (36%) increase in global mean leaf area index, which is most pronounced at the middle and high northern latitudes. Vegetation feedback has little effect on globally averaged surface temperature. On a regional scale, however, it induces statistically significant changes in surface temperature, in particular over most parts of continental Eurasia east of about 60°E where annual surface temperature is expected to increase by 0.1?C1.0?K, with an average of about 0.4?K for each future period. These changes can mostly be explained by changes in surface albedo resulting from vegetation changes in the context of future global warming.     

A regional climate model downscaling projection of China future climate change

Climate changes over China from the present (1990–1999) to future (2046–2055) under the A1FI (fossil fuel intensive) and A1B (balanced) emission scenarios are projected using the Regional Climate Model version 3 (RegCM3) nests with the National Center for Atmospheric Research (NCAR) Community Climate System Model (CCSM). For the present climate, RegCM3 downscaling corrects several major deficiencies in the driving CCSM, especially the wet and cold biases over the Sichuan Basin. As compared with CCSM, RegCM3 produces systematic higher spatial pattern correlation coefficients with observations for precipitation and surface air temperature except during winter. The projected future precipitation changes differ largely between CCSM and RegCM3, with strong regional and seasonal dependence. The RegCM3 downscaling produces larger regional precipitation trends (both decreases and increases) than the driving CCSM. Contrast to substantial trend differences projected by CCSM, RegCM3 produces similar precipitation spatial patterns under different scenarios except autumn. Surface air temperature is projected to consistently increase by both CCSM and RegCM3, with greater warming under A1FI than A1B. The result demonstrates that different scenarios can induce large uncertainties even with the same RCM-GCM nesting system. Largest temperature increases are projected in the Tibetan Plateau during winter and high-latitude areas in the northern China during summer under both scenarios. This indicates that high elevation and northern regions are more vulnerable to climate change. Notable discrepancies for precipitation and surface air temperature simulated by RegCM3 with the driving conditions of CCSM versus the model for interdisciplinary research on climate under the same A1B scenario further complicated the uncertainty issue. The geographic distributions for precipitation difference among various simulations are very similar between the present and future climate with very high spatial pattern correlation coefficients. The result suggests that the model present climate biases are systematically propagate into the future climate projections. The impacts of the model present biases on projected future trends are, however, highly nonlinear and regional specific, and thus cannot be simply removed by a linear method. A model with more realistic present climate simulations is anticipated to yield future climate projections with higher credibility.     

Atmospheric circulation anomalies due to 115 kyr BP climate forcing dominated by changes in the North Pacific Ocean

Climate at the time of inception of the Laurentide Ice Sheet (LIS) at ~115 kyr BP is simulated with the fully coupled NCAR Community Climate System Model (CCSM3) and compared to a simulated preindustrial climate (circa 1870) in order to better understand land surface and atmospheric responses to orbital and greenhouse cooling at inception. The interaction between obliquity and eccentricity produces maximum decrease in TOA insolation in JJA over the Arctic but increases occur over the tropics in DJF. The land surface response is dominated by widespread summer cooling in the Northern Hemisphere (NH), increases in snowfall, and decreases in melt rates and total precipitation. CCSM3 responds to the climate forcing at 115 kyr BP by producing incipient glaciation in the areas of LIS nucleation. We find that the inception of the LIS could have occurred with atmospheric circulation patterns that differ little from the present. The location of the troughs/ridges, mean flow over the Canadian Arctic and dominant modes of the atmospheric circulation are all very similar to the present. Larger changes in mean sea level pressure occur upstream of the inception region in the North Pacific Ocean and downstream in Western Europe. In the North Pacific region, the 115 kyr BP anomalies weaken both the Pacific high and Aleutian low making NH summers look more like the PREIND winters and vice versa. The occurrence of cold JJA anomalies at 115 kyr BP favors outbreaks of cold air not in the winter as in contemporary climates but during the summer instead and reinforces the cooling from orbital and GHG reductions. Increased poleward eddy transport of heat and moisture characterizes the atmospheric response in addition to reduced total cloud cover in the Arctic.     

Future evolution of the Sahel precipitation zonal contrast in CESM1

The main focus of this study is the zonal contrast of the Sahel precipitation shown in the CMIP5 climate projections: precipitation decreases over the western Sahel (i.e., Senegal and western Mali) and increases over the central Sahel (i.e., eastern Mali, Burkina Faso and Niger). This zonal contrast in future precipitation change is a robust model response to climate change but suffers from a lack of an explanation. To this aim, we study the impact of current and future climate change on Sahel precipitation by using the Large Ensemble of the Community Earth System Model version 1 (CESM1). In CESM1, global warming leads to a strengthening of the zonal contrast, as shown by the difference between the 2060–2099 period (under a high emission scenario) and the 1960–1999 period (under the historical forcing). The zonal contrast is associated with dynamic shifts in the atmospheric circulation. We show that, in absence of a forced response, that is, when only accounting for internal climate variability, the zonal contrast is associated with the Pacific and the tropical Atlantic oceans variability. However, future patterns in sea surface temperature (SST) anomalies are not necessary to explaining the projected strengthening of the zonal contrast. The mechanisms underlying the simulated changes are elucidated by analysing a set of CMIP5 idealised simulations. We show the increase in precipitation over the central Sahel to be mostly associated with the surface warming over northern Africa, which favour the displacement of the monsoon cell northwards. Over the western Sahel, the decrease in Sahel precipitation is associated with a southward shift of the monsoon circulation, and is mostly due to the warming of the SST. These two mechanisms allow explaining the zonal contrast in precipitation change.

     

The potential to narrow uncertainty in projections of regional precipitation change

We separate and quantify the sources of uncertainty in projections of regional (～2,500 km) precipitation changes for the twenty-first century using the CMIP3 multi-model ensemble, allowing a direct comparison with a similar analysis for regional temperature changes. For decadal means of seasonal mean precipitation, internal variability is the dominant uncertainty for predictions of the first decade everywhere, and for many regions until the third decade ahead. Model uncertainty is generally the dominant source of uncertainty for longer lead times. Scenario uncertainty is found to be small or negligible for all regions and lead times, apart from close to the poles at the end of the century. For the global mean, model uncertainty dominates at all lead times. The signal-to-noise ratio (S/N) of the precipitation projections is highest at the poles but less than 1 almost everywhere else, and is far lower than for temperature projections. In particular, the tropics have the highest S/N for temperature, but the lowest for precipitation. We also estimate a ‘potential S/N’ by assuming that model uncertainty could be reduced to zero, and show that, for regional precipitation, the gains in S/N are fairly modest, especially for predictions of the next few decades. This finding suggests that adaptation decisions will need to be made in the context of high uncertainty concerning regional changes in precipitation. The potential to narrow uncertainty in regional temperature projections is far greater. These conclusions on S/N are for the current generation of models; the real signal may be larger or smaller than the CMIP3 multi-model mean. Also note that the S/N for extreme precipitation, which is more relevant for many climate impacts, may be larger than for the seasonal mean precipitation considered here.     

Seasonal differences of model predictability and the impact of SST in the Pacific

Both seasonal potential predictability and the impact of SST in the Pacific on the forecast skill over China are investigated by using a 9-level global atmospheric general circulation model developed at the Institute of Atmospheric Physics under the Chinese Academy of Sciences (IAP9L-AGCM). For each year during 1970 to 1999, the ensemble consists of seven integrations started from consecutive observational daily atmospheric fields and forced by observational monthly SST. For boreal winter, spring and summer,the variance ratios of the SST-forced variability to the total variability and the differences in the spatial correlation coefficients of seasonal mean fields in special years versus normal years are computed respectively. It follows that there are slightly inter-seasonal differences in the model potential predictability in the Tropics. At northern middle and high latitudes, prediction skill is generally low in spring and relatively high either in summer for surface air temperature and middle and upper tropospheric geopotential height or in winter for wind and precipitation. In general, prediction skill rises notably in western China, especially in northwestern China, when SST anomalies (SSTA) in the Nino-3 region are significant. Moreover,particular attention should be paid to the SSTA in the North Pacific (NP) if one aims to predict summer climate over the eastern part of China, i.e., northeastern China, North China and southeastern China.     

Evaluation of an ensemble of regional climate model simulations over South America driven by the ERA-Interim reanalysis: model performance and uncertainties

The capability of a set of 7 coordinated regional climate model simulations performed in the framework of the CLARIS-LPB Project in reproducing the mean climate conditions over the South American continent has been evaluated. The model simulations were forced by the ERA-Interim reanalysis dataset for the period 1990–2008 on a grid resolution of 50 km, following the CORDEX protocol. The analysis was focused on evaluating the reliability of simulating mean precipitation and surface air temperature, which are the variables most commonly used for impact studies. Both the common features and the differences among individual models have been evaluated and compared against several observational datasets. In this study the ensemble bias and the degree of agreement among individual models have been quantified. The evaluation was focused on the seasonal means, the area-averaged annual cycles and the frequency distributions of monthly means over target sub-regions. Results show that the Regional Climate Model ensemble reproduces adequately well these features, with biases mostly within ±2 °C and ±20 % for temperature and precipitation, respectively. However, the multi-model ensemble depicts larger biases and larger uncertainty (as defined by the standard deviation of the models) over tropical regions compared with subtropical regions. Though some systematic biases were detected particularly over the La Plata Basin region, such as underestimation of rainfall during winter months and overestimation of temperature during summer months, every model shares a similar behavior and, consequently, the uncertainty in simulating current climate conditions is low. Every model is able to capture the variety in the shape of the frequency distribution for both temperature and precipitation along the South American continent. Differences among individual models and observations revealed the nature of individual model biases, showing either a shift in the distribution or an overestimation or underestimation of the range of variability.     

欧亚大陆积雪对我国春季气候可预报性的影响

利用大气环流模式IAP9L_CoLM,通过两组集合后报试验,考察了欧亚大陆积雪对我国春季气候可预报性的影响。一组试验为常规后报试验,积雪是由模式陆面过程预报得到的,另一组试验为积雪试验,模式积分过程中欧亚大陆雪水当量由微波遥感积雪资料替代,一天替换一次。通过分析两组试验后报结果的差异,来考察欧亚大陆积雪对我国春季（3~5月）气候可预报性的影响。分析表明:欧亚大陆积雪模拟水平的改善能提高春季欧亚大陆中高纬环流场（海平面气压场和中、高层位势高度场）的可预报性,模式对我国春季气温异常的年际变化和空间分布的可预报能力也有显著增强。对我国春季降水,虽然预报技巧较低,但引入较真实的欧亚积雪作用后,由于中高纬环流场预报技巧的改进导致降水的预测能力也有所改进。个例分析也表明,欧亚中高纬春季积雪异常模拟水平的改善导致了欧亚中高纬贝加尔湖及以南区域环流场可预报性的提高,最终使中国东部区域春季气候异常模拟技巧得以改善。以上结果也证实,欧亚大陆积雪是影响东亚区域春季气候的一个重要因子,要提高模式对中国春季气候的预报技巧,积雪模拟水平的改进是非常必要的。     

The relative contributions of radiative forcing and internal climate variability to the late 20th Century winter drying of the Mediterranean region

The roles of anthropogenic climate change and internal climate variability in causing the Mediterranean region’s late 20th Century extended winter drying trend are examined using 19 coupled models from the Intergovernmental Panel on Climate Change Fourth Assessment Report. The observed drying was influenced by the robust positive trend in the North Atlantic Oscillation (NAO) from the 1960s to the 1990s. Model simulations and observations are used to assess the probable relative roles of radiative forcing, and internal variability in explaining the circulation trend that drove much of the precipitation change. Using the multi-model ensemble we assess how well the models can produce multidecadal trends of realistic magnitude, and apply signal-to-noise maximizing EOF analysis to obtain a best estimate of the models’ (mean) sea-level pressure (SLP) and precipitation responses to changes in radiative forcing. The observed SLP and Mediterranean precipitation fields are regressed onto the timeseries associated with the models’ externally forced pattern and the implied linear trends in both fields between 1960 and 1999 are calculated. It is concluded that the radiatively forced trends are a small fraction of the total observed trends. Instead it is argued that the robust trends in the observed NAO and Mediterranean rainfall during this period were largely due to multidecadal internal variability with a small contribution from the external forcing. Differences between the observed and NAO-associated precipitation trends are consistent with those expected as a response to radiative forcing. The radiatively forced trends in circulation and precipitation are expected to strengthen in the current century and this study highlights the importance of their contribution to future precipitation changes in the region.     

SST and circulation trend biases cause an underestimation of European precipitation trends

Clear precipitation trends have been observed in Europe over the past century. In winter, precipitation has increased in north-western Europe. In summer, there has been an increase along many coasts in the same area. Over the second half of the past century precipitation also decreased in southern Europe in winter. An investigation of precipitation trends in two multi-model ensembles including both global and regional climate models shows that these models fail to reproduce the observed trends. In many regions the model spread does not cover the trend in the observations. In contrast, regional climate model (RCM) experiments with observed boundary conditions reproduce the observed precipitation trends much better. The observed trends are largely compatible with the range of uncertainties spanned by the ensemble, indicating that the boundary conditions of RCMs are responsible for large parts of the trend biases. We find that the main factor in setting the trend in winter is atmospheric circulation, for summer sea surface temperature (SST) is important in setting precipitation trends along the North Sea and Atlantic coasts. The causes of the large trends in atmospheric circulation and summer SST are not known. For SST there may be a connection with the well-known ocean circulation biases in low-resolution ocean models. A quantitative understanding of the causes of these trends is needed so that climate model based projections of future climate can be corrected for these precipitation trend biases.     

Internal variability in multidecadal trends of surface air temperature over antarctica in austral winter in model simulations

The surface air temperature (SAT) exhibits pronounced warming over West Antarctica in recent decades, especially in austral spring and winter. Using a 30-member ensemble of simulations by Community Earth System Model (CESM), two reanalysis datasets, and observed station data, this study investigates the relative contributions of internally generated low-frequency climate variability and externally forced climate change to the austral winter SAT trend in Antarctica. Although these simulations share the same external forcing, the SAT trends during 1979–2005 show large diversity among the individual members in the CESM ensemble simulations, suggesting that internally generated variability contributes a considerable part to the multidecadal SAT change in Antarctica. Quantitatively, the total forced contribution to the SAT (1979–2005) change is about 0.53 k/27 yr, and the internal variability can be strong enough to double or cancel the externally forced warming trend. A method called “dynamical adjustment” is utilized to further divide the forced response. We find both the forced thermodynamically-induced and the forced dynamically-induced SAT trends are positive over all the regions in Antarctica, with the regional mean values of 0.20 k /27 yr and 0.33 k/27 yr, respectively. The diversity of SAT trends among the simulations is closely linked to a Southern hemisphere Annular Mode (SAM)-like atmospheric circulation multidecadal change in the Southern Hemisphere. When there exists a positive–negative seesaw of pressure trend between Antarctica and the mid-latitudes, the SAT trend is positive over most of Antarctica but negative over the Antarctic Peninsula, and vice versa. The SAM-like atmospheric circulation multidecadal change mainly arises from atmospheric internal variability rather than remote tropical Sea Surface Temperature (SST).