A scenario of European climate change for the late twenty-first century: seasonal means and interannual variability

A scenario of European climate change for the late twenty-first century is described, using a high-resolution state-of-the-art model. A time-slice approach is used, whereby the atmospheric general circulation model, HadAM3P, was integrated for two periods, 1960–1990 and 2070–2100, using the SRES A2 scenario. For the first time an ensemble of such experiments was produced, along with appropriate statistical tests for assessing significance. The focus is on changes to the statistics of seasonal means, and includes analysis of both multi-year means and interannual variance. All four seasons are assessed, and anomalies are mapped for surface air temperature, precipitation and snow mass. Mechanisms are proposed where these are dominated by straightforward local processes. In winter, the largest warming occurs over eastern Europe, up to 7°C, mean snow mass is reduced by at least 80% except over Scandinavia, and precipitation increases over all but the southernmost parts of Europe. In summer, temperatures rise by 6–9°C south of about 50°N, and mean rainfall is substantially reduced over the same area. In spring and autumn, anomalies tend to be weaker, but often display patterns similar to the preceding season, reflecting the inertia of the land surface component of the climate system. Changes in interannual variance are substantial in the solsticial seasons for many regions (note that for precipitation, variance estimates are scaled by the square of the mean). In winter, interannual variability of near-surface air temperature is considerably reduced over much of Europe, and the relative variability of precipitation is reduced north of about 50°N. In summer, the (relative) interannual variance of both variables increases over much of the continent.     

Estimating present climate in a warming world: a model-based approach

Weather services base their operational definitions of “present” climate on past observations, using a 30-year normal period such as 1961–1990 or 1971–2000. In a world with ongoing global warming, however, past data give a biased estimate of the actual present-day climate. Here we propose to correct this bias with a “delta change” method, in which model-simulated climate changes and observed global mean temperature changes are used to extrapolate past observations forward in time, to make them representative of present or future climate conditions. In a hindcast test for the years 1991–2002, the method works well for temperature, with a clear improvement in verification statistics compared to the case in which the hindcast is formed directly from the observations for 1961–1990. However, no improvement is found for precipitation, for which the signal-to-noise ratio between expected anthropogenic changes and interannual variability is much lower than for temperature. An application of the method to the present (around the year 2007) climate suggests that, as a geographical average over land areas excluding Antarctica, 8–9 months per year and 8–9 years per decade can be expected to be warmer than the median for 1971–2000. Along with the overall warming, a substantial increase in the frequency of warm extremes at the expense of cold extremes of monthly-to-annual temperature is expected.     

Modelling daily temperature extremes: recent climate and future changes over Europe

Probability distributions of daily maximum and minimum temperatures in a suite of ten RCMs are investigated for (1) biases compared to observations in the present day climate and (2) climate change signals compared to the simulated present day climate. The simulated inter-model differences and climate changes are also compared to the observed natural variability as reflected in some very long instrumental records. All models have been forced with driving conditions from the same global model and run for both a control period and a future scenario period following the A2 emission scenario from IPCC. We find that the bias in the fifth percentile of daily minimum temperatures in winter and at the 95th percentile of daily maximum temperature during summer is smaller than 3 (±5°C) when averaged over most (all) European sub-regions. The simulated changes in extreme temperatures both in summer and winter are larger than changes in the median for large areas. Differences between models are larger for the extremes than for mean temperatures. A comparison with historical data shows that the spread in model predicted changes in extreme temperatures is larger than the natural variability during the last centuries.     

Variability in annual mean circulation in southern high latitudes

 Using a hierarchy of climate models together with observations from gridded analyses, I examine the atmosphere-only and coupled ocean-atmosphere variability in the general circulation for the region south of 40 °S. The variability in mean sea level pressure (MSLP) is well simulated by the coupled models. A complication is that the difference between the two analyses used for verification is comparable to the analysis-model differences. An increase in variability is seen within the hierarchy of model runs although even a model without interannual variations in sea surface temperatures (SSTs) captures most of the observed variability. The temporal variation in MSLP in southern high latitudes has a white spectrum consistent with “random” forcing by weather events and a decoupling from oceanic “integration”. In contrast, the spatial pattern of MSLP variability shows large-scale structure that is consistent between observations and various models, even without interannual variation in SSTs. This shows that the models are sufficiently skillful to reproduce the pattern of observed variability and suggests that the pattern of variability is a characteristic of the land-sea distribution and topography. Received: 18 December 1996/Accepted: 23 May 1997     

Climate Variability-Observations, Reconstructions, and Model Simulations for the Atlantic-European and Alpine Region from 1500-2100 AD

A detailed analysis is undertaken of the Atlantic-European climate using data from 500-year-long proxy-based climate reconstructions, a long climate simulation with perpetual 1990 forcing, as well as two global and one regional climate change scenarios. The observed and simulated interannual variability and teleconnectivity are compared and interpreted in order to improve the understanding of natural climate variability on interannual to decadal time scales for the late Holocene. The focus is set on the Atlantic-European and Alpine regions during the winter and summer seasons, using temperature, precipitation, and 500 hPa geopotential height fields. The climate reconstruction shows pronounced interdecadal variations that appear to “lock” the atmospheric circulation in quasi-steady long-term patterns over multi-decadal periods controlling at least part of the temperature and precipitation variability. Different circulation patterns are persistent over several decades for the period 1500 to 1900. The 500-year-long simulation with perpetual 1990 forcing shows some substantial differences, with a more unsteady teleconnectivity behaviour. Two global scenario simulations indicate a transition towards more stable teleconnectivity for the next 100 years. Time series of reconstructed and simulated temperature and precipitation over the Alpine region show comparatively small changes in interannual variability within the time frame considered, with the exception of the summer season, where a substantial increase in interannual variability is simulated by regional climate models.     

Interannual variations of the boreal summer intraseasonal variability predicted by ten atmosphere–ocean coupled models

The reproducibility of boreal summer intraseasonal variability (ISV) and its interannual variation by dynamical models are assessed through diagnosing 21-year retrospective forecasts from ten state-of-the-art ocean–atmosphere coupled prediction models. To facilitate the assessment, we have defined the strength of ISV activity by the standard deviation of 20–90 days filtered precipitation during the boreal summer of each year. The observed climatological ISV activity exhibits its largest values over the western North Pacific and Indian monsoon regions. The notable interannual variation of ISV activity is found primarily over the western North Pacific in observation while most models have the largest variability over the central tropical Pacific and exhibit a wide range of variability in spatial patterns that are different from observation. Although the models have large systematic biases in spatial pattern of dominant variability, the leading EOF modes of the ISV activity in the models are closely linked to the models’ El Nino-Southern Oscillation (ENSO), which is a feature that resembles the observed ISV and ENSO relationship. The ENSO-induced easterly vertical shear anomalies in the western and central tropical Pacific, where the summer mean vertical wind shear is weak, result in ENSO-related changes of ISV activity in both observation and models. It is found that the principal components of the predicted dominant modes of ISV activity fluctuate in a very similar way with observed ones. The model biases in the dominant modes are systematic and related to the external SST forcing. Thus the statistical correction method of this study based on singular value decomposition is capable of removing a large portion of the systematic errors in the predicted spatial patterns. The 21-year-averaged pattern correlation skill increases from 0.25 to 0.65 over the entire Asian monsoon region after applying the bias correction method to the multi-model ensemble mean prediction.     

Sensitivity of European glaciers to precipitation and temperature – two case studies

A nonlinear backpropagation network (BPN) has been trained with high-resolution multiproxy reconstructions of temperature and precipitation (input data) and glacier length variations of the Alpine Lower Grindelwald Glacier, Switzerland (output data). The model was then forced with two regional climate scenarios of temperature and precipitation derived from a probabilistic approach: The first scenario (“no change”) assumes no changes in temperature and precipitation for the 2000–2050 period compared to the 1970–2000 mean. In the second scenario (“combined forcing”) linear warming rates of 0.036–0.054°C per year and changing precipitation rates between −17% and +8% compared to the 1970–2000 mean have been used for the 2000–2050 period. In the first case the Lower Grindelwald Glacier shows a continuous retreat until the 2020s when it reaches an equilibrium followed by a minor advance. For the second scenario a strong and continuous retreat of approximately −30 m/year since the 1990s has been modelled. By processing the used climate parameters with a sensitivity analysis based on neural networks we investigate the relative importance of different climate configurations for the Lower Grindelwald Glacier during four well-documented historical advance (1590–1610, 1690–1720, 1760–1780, 1810–1820) and retreat periods (1640–1665, 1780–1810, 1860–1880, 1945–1970). It is shown that different combinations of seasonal temperature and precipitation have led to glacier variations. In a similar manner, we establish the significance of precipitation and temperature for the well-known early eighteenth century advance and the twentieth century retreat of Nigardsbreen, a glacier in western Norway. We show that the maritime Nigardsbreen Glacier is more influenced by winter and/or spring precipitation than the Lower Grindelwald Glacier.     

RegCM3 regional climatologies for South America using reanalysis and ECHAM global model driving fields

To enable downscaling of seasonal prediction and climate change scenarios, long-term baseline regional climatologies which employ global model forcing are needed for South America. As a first step in this process, this work examines climatological integrations with a regional climate model using a continental scale domain nested in both reanalysis data and multiple realizations of an atmospheric general circulation model (GCM). The analysis presents an evaluation of the nested model simulated large scale circulation, mean annual cycle and interannual variability which is compared against observational estimates and also with the driving GCM for the Northeast, Amazon, Monsoon and Southeast regions of South America. Results indicate that the regional climate model simulates the annual cycle of precipitation well in the Northeast region and Monsoon regions; it exhibits a dry bias during winter (July–September) in the Southeast, and simulates a semi-annual cycle with a dry bias in summer (December–February) in the Amazon region. There is little difference in the annual cycle between the GCM and renalyses driven simulations, however, substantial differences are seen in the interannual variability. Despite the biases in the annual cycle, the regional model captures much of the interannual variability observed in the Northeast, Southeast and Amazon regions. In the Monsoon region, where remote influences are weak, the regional model improves upon the GCM, though neither show substantial predictability. We conclude that in regions where remote influences are strong and the global model performs well it is difficult for the regional model to improve the large scale climatological features, indeed the regional model may degrade the simulation. Where remote forcing is weak and local processes dominate, there is some potential for the regional model to add value. This, however, will require improvments in physical parameterizations for high resolution tropical simulations.     

Twenty-first century Arctic climate change in the CCSM3 IPCC scenario simulations

Arctic climate change in the Twenty-first century is simulated by the Community Climate System Model version 3.0 (CCSM3). The simulations from three emission scenarios (A2, A1B and B1) are analyzed using eight (A1B and B1) or five (A2) ensemble members. The model simulates a reasonable present-day climate and historical climate trend. The model projects a decline of sea-ice extent in the range of 1.4–3.9% per decade and 4.8–22.2% per decade in winter and summer, respectively, corresponding to the range of forcings that span the scenarios. At the end of the Twenty-first century, the winter and summer Arctic mean surface air temperature increases in a range of 4–14°C (B1 and A2) and 0.7–5°C (B1 and A2) relative to the end of the Twentieth century. The Arctic becomes ice-free during summer at the end of the Twenty-first century in the A2 scenario. Similar to the observations, the Arctic Oscillation (AO) is the dominant factor in explaining the variability of the atmosphere and sea ice in the 1870–1999 historical runs. The AO shifts to the positive phase in response to greenhouse gas forcings in the Twenty-first century. But the simulated trends in both Arctic mean sea-level pressure and the AO index are smaller than what has been observed. The Twenty-first century Arctic warming mainly results from the radiative forcing of greenhouse gases. The 1st empirical orthogonal function (explains 72.2–51.7% of the total variance) of the wintertime surface air temperature during 1870–2099 is characterized by a strong warming trend and a “polar amplification”-type of spatial pattern. The AO, which plays a secondary role, contributes to less than 10% of the total variance in both surface temperature and sea-ice concentration.     

The energy cycle in atmospheric models

The energy cycle characterizes basic aspects of the physical behaviour of the climate system. Terms in the energy cycle involve first and second order climate statistics (means, variances, covariances) and the intercomparison of energetic quantities offers physically motivated “second order” insight into model and system behaviour. The energy cycle components of 12 models participating in AMIP2 are calculated, intercompared and assessed against results based on NCEP and ERA reanalyses. In general, models simulate a modestly too vigorous energy cycle and the contributions to and reasons for this are investigated. The results suggest that excessive generation of zonal available potential energy is an important driver of the overactive energy cycle through “generation push” while excessive dissipation of eddy kinetic energy in models is implicated through “dissipation pull‘’. The study shows that “ensemble model” results are best or among the best in the comparison of energy cycle quantities with reanalysis-based values. Thus ensemble approaches are apparently “best” not only for the simulation of 1st order climate statistics as in Lambert and Boer (Clim Dyn 17:83–106, 2001) but also for the higher order climate quantities entering the energy cycle.     

Assessing the impact of climate change on representative field crops in Israeli agriculture: a case study of wheat and cotton

Climate changes, associated with accumulation of greenhouse gases, are expected to have a profound influence on agricultural sustainability in Israel, a semi-arid area characterized by a cold wet winter and a dry warm summer. Accordingly this study explored economic aspects of agricultural production under projected climate-change scenarios by the “production function” approach, as applied to two representative crops: wheat, as the major crop grown in Israel’s dry southern region, and cotton, representing the more humid climate in the north. Adjusting outputs of the global climate model HadCM3 to the specific research locations, we generated projections for 2070–2100 temperatures and precipitations for two climate change scenarios. Results for wheat vary among climate scenarios; net revenues become negative under the severe scenario (change from −145 to −273%), but may increase under the moderate one (−43 to +35%), depending on nitrogen applied to the crop. Distribution of rain events was found to play a major role in determining yields. By contrast, under both scenarios cotton experiences a considerable decrease in yield with significant economic losses (−240 and −173% in A2 and B2 scenarios, respectively). Additional irrigation and nitrogen may reduce farming losses, unlike changes in seeding dates.     

Temperature scenarios for Norway: from regional to local scale

Scenarios with daily time resolution are frequently used in research on the impacts of climate change. These are traditionally developed by regional climate models (RCMs). The spatial resolution, however, is usually too coarse for local climate change analysis, especially in regions with complex topography, such as Norway. The RCM used, HIRHAM, is run with lateral boundary forcing provided from two global medium resolution models; the ECHAM4/OPYC3 from MPI and the HadAM3H from the Hadley centre. The first is run with IPCC SRES emission scenario B2, the latter is run with IPCC SRES emission scenarios A2 and B2. All three scenarios represent the future time period 2071–2100. Both models have a control run, representing the present climate (1961–1990). Daily temperature scenarios are interpolated from HIRHAM to Norwegian temperature stations. The at-site HIRHAM-temperatures, both for the control and scenario runs, are adjusted to be locally representative. Mean monthly values and standard deviations based on daily values of the adjusted HIRHAM-temperatures, as well as the cumulative distribution curve of daily seasonal temperatures, are conclusive with observations for the control period. Residual kriging are used on the adjusted daily HIRHAM-temperatures to obtain high spatial temperature scenarios. Mean seasonal temperature grids are obtained. By adjusting the control runs and scenarios and improving the spatial resolution of the scenarios, the absolute temperature values are representative at a local scale. The scenarios indicate larger warming in winter than in summer in the Scandinavian regions. A marked west–east and south–north gradient is projected for Norway, where the largest increase is in eastern and northern regions. The temperature of the coldest winter days is projected to increase more than the warmer temperatures.     

The role of ocean thermodynamics and dynamics in Asian summer monsoon changes during the mid-Holocene

Studies of climate change 6,000 years before present using atmospheric general circulation models (AGCMs) suggest the enhancement and northward shift of the summer Asian and African monsoons in the Northern Hemisphere. Although enhancement of the African monsoonal precipitation by ocean coupling is a common and robust feature, contradictions exist between analyses of the role of the ocean in the strength of the Asian monsoon. We investigated the role of the ocean in the Asian monsoon and sought to clarify which oceanic mechanisms played an important role using three ocean coupling schemes: MIROC, an atmosphere–ocean coupled general circulation model [C]; an AGCM extracted from MIROC coupled with a mixed-layer ocean model [M]; and the same AGCM, but with prescribed sea surface temperatures [A]. The effect of “ocean dynamics” is quantified through differences between experiments [C] and [M]. The effect of “ocean thermodynamics” is quantified through differences between experiments [M] and [A]. The precipitation change for the African and Asian monsoon area suggested that the ocean thermodynamics played an important role. In particular, the enhancement of the Asian monsoonal precipitation was most vigorous in the AGCM simulations, but mitigated in early summer in ocean coupled cases, which were not significantly different from each other. The ocean feedbacks were not significant for the precipitation change in late summer. On the other hand, in Africa, ocean thermodynamics contributed to the further enhancement of the precipitation from spring to autumn, and the ocean dynamics had a modest impact in enhancing precipitation in late summer.     

Resolution effects on regional climate model simulations of seasonal precipitation over Europe

We analyze a set of nine regional climate model simulations for the period 1961–2000 performed at 25 and 50 km horizontal grid spacing over a European domain in order to determine the effects of horizontal resolution on the simulation of precipitation. All of the models represent the seasonal mean spatial patterns and amount of precipitation fairly well. Most models exhibit a tendency to over-predict precipitation, resulting in a domain-average total bias for the ensemble mean of about 20% in winter (DJF) and less than 10% in summer (JJA) at both resolutions, although this bias could be artificially enhanced by the lack of a gauge correction in the observations. A majority of the models show increased precipitation at 25 km relative to 50 km over the oceans and inland seas in DJF, JJA, and ANN (annual average), although the response is strongest during JJA. The ratio of convective precipitation to total precipitation decreases over land for most models at 25 km. In addition, there is an increase in interannual variability in many of the models at 25 km grid spacing. Comparison with gridded observations indicates that a majority of models show improved skill in simulating both the spatial pattern and temporal evolution of precipitation at 25 km compared to 50 km during the summer months, but not in winter or on an annual mean basis. Model skill at higher resolution in simulating the spatial and temporal character of seasonal precipitation is found especially for Great Britain. This geographic dependence of the increased skill suggests that observed data of sufficient density are necessary to capture fine-scale climate signals. As climate models increase their horizontal resolution, it is thus a key priority to produce high quality fine scale observations for model evaluation.     

Simulation of the summer circulation over South America by two regional climate models. Part II: A comparison between 1997/1998 El Niño and 1998/1999 La Niña events

Summary This study investigates the capabilities of two regional models (the ICTP RegCM3 and the climate version of the CPTEC Eta model – EtaClim) in simulating the summer quasi-stationary circulations over South America during two extreme cases: the 1997–1998 El Ni?o and 1998–1999 La Ni?a. The results showed that both the models are successful in simulating the interannual variability of summer quasi-stationary circulation over South America. Both the models simulated the intensification of subtropical jet stream during the El Ni?o event, which favoured the blocking of transient systems and increased the precipitation over south Brazil. The models simulated the increase (decrease) of precipitation over north (west) Amazonia during the La Ni?a (El Ni?o) event. The upper level circulation is in agreement with the simulated distribution of precipitation. In general, the results showed that both the models are capable of capturing the main changes of the summer climate over South America during these two extreme cases and consequently they have potential to predict climate anomalies.     

Sensitivity studies of the RegCM3 simulation of summer precipitation, temperature and local wind field in the Caribbean Region

Summary We present a preliminary evaluation of the performance of three different cumulus parameterization schemes in the ICTP Regional Climate Model RegCM3 for two overlapping domains (termed “big” and “small”) and horizontal resolutions (50 and 25 km) in the Caribbean area during the summer (July–August–September). The cumulus parameterizations were the Grell scheme with two closure assumptions (Arakawa–Schubert and Fritsch–Chappell) and the Anthes-Kuo scheme. An additional sensitivity test was performed by comparing two different flux parameterization schemes over the ocean (Zeng and BATS). There is a systematic underestimation of air temperature and precipitation when compared with analyzed data over the big domain area. Greater (∼2 °C) and smaller (∼0.9 °C) negative temperature biases are obtained in Grell–FC and Kuo convective scheme, respectively, and intermediate values are obtained in Grell–AS. The small domain simulation produces results substantially different, both for air temperature and precipitation. Temperature estimations are better for the small domain, while the precipitation estimations are better for the big domain. An additional experiment showed that by using BATS to calculate the ocean fluxes in the big domain instead of the Zeng scheme, precipitation increases by 25% and the share of convective precipitation rose from 18% to 45% of the total, which implies a better simulation of precipitation. These changes were attributed to an increase of near surface latent heating when using BATS over the ocean. The use of BATS also reduces the cold bias by about 0.3–0.4 °C, associated with an increase of minimum temperature. The behavior of the precipitation diurnal cycle and its relation with sea breeze was investigated in the small domain experiments. Results showed that the Grell–Arakawa–Schubert closure describes better this circulation as compared with Grell–Fritsch–Chappell closure.     

Intercomparsion of regional biases and doubled CO2-sensitivity of coupled atmosphere-ocean general circulation model experiments

 We compared regional biases and transient doubled CO₂ sensitivities of nine coupled atmosphere-ocean general circulation models (GCMs) from six international climate modeling groups. We evaluated biases and responses in winter and summer surface air temperatures and precipitation for seven subcontinental regions, including those in the 1990 Intergovernmental Panel on Climate Change (IPCC) Scientific Assessment. Regional biases were large and exceeded the variance among four climatological datasets, indicating that model biases were not primarily due to uncertainty in observations. Model responses to altered greenhouse forcing were substantial (average temperature change=2.7±0.9 ^°C, range of precipitation change =−35 to +120% of control). While coupled models include more climate system feedbacks than earlier GCMs implemented with mixed-layer ocean models, inclusion of a dynamic ocean alone did not improve simulation of long-term mean climatology nor increase convergence among model responses to altered greenhouse gas forcing. On the other hand, features of some of the coupled models including flux adjustment (which may have simply masked simulation errors), high horizontal resolution, and estimation of screen height temperature contributed to improved simulation of long-term surface climate. The large range of model responses was partly accounted for by inconsistencies in forcing scenarios and transient-simulation averaging periods. Nonetheless, the models generally had greater agreement in their sensitivities than their controls did with observations. This suggests that consistent, large-scale response features from an ensemble of model sensitivity experiments may not depend on details of their representation of present-day climate. Received: 9 September 1996 / Revised: 31 July 1997     

The coupling of optimal economic growth and climate dynamics

In this paper, we study optimal economic growth programs coupled with climate change dynamics. The study is based on models derived from MERGE, a well established integrated assessment model (IAM). We discuss first the introduction in MERGE of a set of “tolerable window” constraints which limit both the temperature change and the rate of temperature change. These constraints, obtained from ensemble simulations performed with the Bern 2.5-D climate model, allow us to identity a domain intended to preserve the Atlantic thermohaline circulation. Next, we report on experiments where a two-way coupling is realized between the economic module of MERGE and an intermediate complexity “3-D-” climate model (C-GOLDSTEIN) which computes the changes in climate and mean temperature. The coupling is achieved through the implementation of an advanced “oracle based optimization technique” which permits the integration of information coming from the climate model during the search for the optimal economic growth path. Both cost-effectiveness and cost-benefit analysis modes are explored with this combined “meta-model” which we refer to as GOLDMERGE. Some perspectives on future implementations of these approaches in the context of “collaborative” or “community” integrated assessment modules are derived from the comparison of the different approaches.     

Climate sensitivity and climate change under strong forcing

A version of the National Centre for Atmospheric Research (NCAR) coupled climate model is integrated under current climate conditions and in a series of experiments with climate forcings ranging from modest to very strong. The purpose of the experiments is to investigate the nature and behaviour of the climate feedback/sensitivity of the model, its evolution with time and climate state, the robustness of model parameterizations as forcing levels increase, and the possibility of a “runaway” warming under strong forcing. The model is integrated for 50 years, or to failure, after increasing the solar constant by 2.5, 10, 15, 25, 35, and 45% of its control value. The model successfully completes 50 years of integration for the 2.5, 10, 15, and 25% solar constant increases but fails for increases of 35% and 45%. The effective global climate sensitivity evolves with time and analysis indicates that a new equilibrium will be obtained for the 2.5, 10, and 15% cases but that runaway warming is underway for the 25% increase in solar constant. Feedback processes are analysed both locally and globally in terms of longwave and shortwave, clear-sky/surface, and cloud forcing components. Feedbacks in the system must be negative overall and of sufficient strength to balance the positive forcing if the system is to attain a new equilibrium. Longwave negative feedback processes strengthen in a reasonably linear fashion as temperature increases but shortwave feedback processes do not. In particular, solar cloud feedback becomes less negative and, for the 25% forcing case, eventually becomes positive, resulting in temperatures that “run away”. The conditions under which a runaway climate warming might occur have previously been investigated using simpler models. For sufficiently strong forcing, the greenhouse effect of increasing water vapour in a warmer atmosphere is expected to overwhelm the negative feedback of the longwave cooling to space as temperature increases. This is not, however, the reason for the climate instability experienced in the GCM. Instead, the model experiences a “cloud feedback” warming whereby the decrease in cloudiness that occurs when temperature increases beyond a critical value results in an increased absorption of solar radiation by the system, leading to the runaway warming.