On the dependence of climate sensitivity on convective parametrization

Two sensitivity experiments, in which CO₂ is doubled and sea-surface temperatures are enhanced, were carried out using a general circulation model to determine the influence of the convective parametrization on simulated climate change. In the first experiment, a non-penetrative layer-swapping convection scheme is used; in the second, a penetrative scheme is used. It is found that the penetrative scheme gives the greater upper tropospheric warming (over 4.5 K compared to 4 K) and the greater reduction in upper tropospheric cloud, consistent with recent CO₂ sensitivity studies. However, there is a 0.7 Wm^–2 greater increase in net downward radiation at the top of the atmosphere in the experiment with the non-penetrative scheme, implying a larger tropical warming which is inconsistent with recent CO₂ studies. Other possible explanations for discrepancies between recent studies of the equilibrium climate response to increasing CO₂ are considered and discussed. The changes in the atmospheric fluxes of heat and moisture from the tropical continents in the model with the penetrative scheme differ from those found using the non-penetrative scheme, and those in an equilibrium experiment using the penetrative scheme. Thus, changes in circulation may explain the apparent discrepancy in the current experiments, but prescribed sea-surface temperature experiments may not provide a reliable indication of a model's equilibrium climate sensitivity. Offprint requests to: JFB Mitchell     

On the generation and interpretation of probabilistic estimates of climate sensitivity

The equilibrium climate response to anthropogenic forcing has long been one of the dominant, and therefore most intensively studied, uncertainties in predicting future climate change. As a result, many probabilistic estimates of the climate sensitivity (S) have been presented. In recent years, most of them have assigned significant probability to extremely high sensitivity, such as P(S?>?6C)?>?5%. In this paper, we investigate some of the assumptions underlying these estimates. We show that the popular choice of a uniform prior has unacceptable properties and cannot be reasonably considered to generate meaningful and usable results. When instead reasonable assumptions are made, much greater confidence in a moderate value for S is easily justified, with an upper 95% probability limit for S easily shown to lie close to 4°C, and certainly well below 6°C. These results also impact strongly on projected economic losses due to climate change.     

On the estimation of systematic error in regression-based predictions of climate sensitivity

An extension of a regression-based methodology for constraining climate forecasts using a multi-thousand member ensemble of perturbed climate models is presented, using the multi-model CMIP-3 ensemble to estimate the systematic model uncertainty in the prediction, with the caveat that systematic biases common to all models are not accounted for. It is shown that previous methodologies for estimating the systematic uncertainty in predictions of climate sensitivity are dependent on arbitrary choices relating to ensemble sampling strategy. Using a constrained regression approach, a multivariate predictor may be derived based upon the mean climatic state of each ensemble member, but components of this predictor are excluded if they cannot be validated within the CMIP-3 ensemble. It is found that the application of the CMIP-3 constraint serves to decrease the upper bound of likelihood for climate sensitivity when compared with previous studies, with 10th and 90th percentiles of probability at 1.5 K and 4.3 K respectively.     

A simple model perturbed physics study of the simulated climate sensitivity uncertainty and its relation to control climate biases

In this study the relationship between climate model biases in the control climate and the simulated climate sensitivity are discussed on the basis of perturbed physics ensemble simulations with a globally resolved energy balance (GREB) model. It is illustrated that the uncertainties in the simulated climate sensitivity (estimated by the transient response to CO₂ forcing scenarios in the twenty first century or idealized 2 × CO₂ forcing experiments) can be conceptually split into two parts: a direct effect of the perturbed physics on the climate sensitivity independent of the control mean climate and an indirect effect of the perturbed physics by changing the control mean climate, which in turn changes the climate sensitivity, as the climate sensitivity itself is depending on the control climate. Biases in the control climate are negatively correlated with the climate sensitivity (colder climates have larger sensitivities), if no physics are perturbed. Perturbed physics that lead to warmer control climate, would in average also lead to larger climate sensitivities, if the control climate is held at the observed reference climate by flux corrections. Thus the effects of control biases and perturbed physics are opposing each other and are partially cancelling each other out. In the GREB model the biases in the control climate are the more important effect for the regional climate sensitivity uncertainties, but for the global mean climate sensitivity both, the biases in the control climate and the perturbed physics, are equally important.     

On the interpretation of inter-model spread in CMIP5 climate sensitivity estimates

This study diagnoses the climate sensitivity, radiative forcing and climate feedback estimates from eleven general circulation models participating in the Fifth Phase of the Coupled Model Intercomparison Project (CMIP5), and analyzes inter-model differences. This is done by taking into account the fact that the climate response to increased carbon dioxide (CO₂) is not necessarily only mediated by surface temperature changes, but can also result from fast land warming and tropospheric adjustments to the CO₂ radiative forcing. By considering tropospheric adjustments to CO₂ as part of the forcing rather than as feedbacks, and by using the radiative kernels approach, we decompose climate sensitivity estimates in terms of feedbacks and adjustments associated with water vapor, temperature lapse rate, surface albedo and clouds. Cloud adjustment to CO₂ is, with one exception, generally positive, and is associated with a reduced strength of the cloud feedback; the multi-model mean cloud feedback is about 33 % weaker. Non-cloud adjustments associated with temperature, water vapor and albedo seem, however, to be better understood as responses to land surface warming. Separating out the tropospheric adjustments does not significantly affect the spread in climate sensitivity estimates, which primarily results from differing climate feedbacks. About 70 % of the spread stems from the cloud feedback, which remains the major source of inter-model spread in climate sensitivity, with a large contribution from the tropics. Differences in tropical cloud feedbacks between low-sensitivity and high-sensitivity models occur over a large range of dynamical regimes, but primarily arise from the regimes associated with a predominance of shallow cumulus and stratocumulus clouds. The combined water vapor plus lapse rate feedback also contributes to the spread of climate sensitivity estimates, with inter-model differences arising primarily from the relative humidity responses throughout the troposphere. Finally, this study points to a substantial role of nonlinearities in the calculation of adjustments and feedbacks for the interpretation of inter-model spread in climate sensitivity estimates. We show that in climate model simulations with large forcing (e.g., 4 × CO₂), nonlinearities cannot be assumed minor nor neglected. Having said that, most results presented here are consistent with a number of previous feedback studies, despite the very different nature of the methodologies and all the uncertainties associated with them.     

Indian agriculture and climate sensitivity

This study estimates the relationship between farm level net-revenue and climate variables in India using cross-sectional evidence. Using the observed reactions of farmers, the study seeks to understand how they have adapted to different climatic conditions across India. District level data is used for the analysis. The study also explores the influence of annual weather and crop prices on the climate response function. The estimated climate response function is used to assess the possible impacts of a ‘best-guess’ climate change scenario on Indian agriculture.     

Climate sensitivity and climate state

The effective climate feedback/sensitivity, including its components, is a robust first order feature of the Canadian Centre for Climate Modelling and Analysis (CCCma) coupled global climate model (GCM) and presumably of the climate system. Feedback/sensitivity characterizes the surface air temperature response to changes in radiative forcing and is constant, to first order, independent of the nature, history, and magnitude of the forcing and of the changing climate state. This "constancy" can only be approximate, however, and modest second order changes of 10–20% are found in stabilization simulations in which the forcing, based on the IS92a scenario, is fixed (stabilized) at year 2050 and 2100 values and the system is integrated for an additional 1000 years toward a new equilibrium. Both positive and negative feedback mechanisms tend to strengthen, with the balance tilted toward stronger negative feedback and hence weaker climate sensitivity, as the system evolves and warms. Some feedback mechanisms weaken locally, however, and an example of such is the ice/snow albedo feedback which is less effective in areas of the Northern Hemisphere where ice/snow has retreated. Changes in the geographical distribution of the feedbacks are modest and weakening feedback in one region is often counteracted by strengthening feedback in other regions so that global and zonal values do not reflect the dominance of a particular mechanism or region but rather the residual of changes in different components and regions. The overall 10–20% strengthening of the negative feedback (decrease in climate sensitivity) in the CCCma model contrasts with a weakening of negative feedback (increase in climate sensitivity) of over 20% in the Hadley Centre model under similar conditions. The different behaviour in the two models is due primarily to solar cloud feedback with a strengthening of the negative solar cloud feedback in the CCCma model contrasting with a weakening of it in the Hadley Centre model. The importance of processes which determine cloud properties and distribution is again manifest both in determining first order climate feedback/sensitivity and also in determining its second order variation with climate state.     

The Great Lakes diversion at Chicago and its implications for climate change

In 1900 the city of Chicago began diverting sizable amounts of water from Lake Michigan to move its sewage down the Illinois River. This diversion launched a series of continuing legal controversies involving Illinois as a defendant against claims by the federal government, various lake states, and Canada who wanted the diversion stopped or drastically reduced. During the past 96 years extended dry periods have lowered the lake levels. Using these dry periods as surrogates for future conditions, their effects on the past controversies were examined as analogs for what might occur as a result of climate change from an enhanced Greenhouse effect. The results reveal that changing socioeconomic factors including population growth will likely cause increased water use, and Chicago will seek additional water from the Great Lakes. New priorities for water use will emerge as in the past. Drier future conditions will likely lead to enhanced diversions from the Great Lakes to serve interests in and outside the basin. Future lower lake levels (reflecting a drier climate) will lead to conflicts related to existing and proposed diversions, and these conflicts would be exacerbated by the consequences of global warming. In any event, a warmer, drier climatic regime will challenge existing laws and institutions for dealing with Great Lakes water issues.     

Energy partitioning at treeline forest and tundra sites and its sensitivity to climate change

Abstract

Summertime energy budgets of contiguous wetland tundra and forest near Churchill, Manitoba along the coast of Hudson Bay were measured over a five year period, 1989–1993. An examination of differences in energy budgets between the two sites showed that net radiation was similar in all years. Soil heat flux was greater at the tundra site in most, but not all, years. However, sensible heat flux was always larger at the forest site and latent heat flux was always greater at the tundra site. Mean daily Bowen ratios at both sites were less than unity in all years. Average Bowen ratios for the five years were 0.45 for tundra and 0.66 for forest. Wind direction is used as an analogue for changing climatic conditions where onshore winds are cooler and moister than offshore winds. Sensible and latent heat fluxes at both sites varied significantly between onshore and offshore wind regimes. However, differences between onshore and offshore fluxes at the tundra site were larger than for the forest. Thus, Bowen ratios also varied more at the tundra site. We have plotted the ratio of tundra‐to‐forest Bowen ratios as a measure of the relative sensitivity of energy partitioning to climatic change. The ratio decreases with increasing vapour pressure deficit (and increasing air temperature). We interpret these results as suggesting that energy partitioning over the wetland tundra is more sensitive to changes in climate than the treeline forest environment. Thus, as the climate warms and becomes drier, more additional energy goes into evaporation of water from the wetland tundra than from the forest.     

Modeling of aerosol induced snow albedo feedbacks over the Himalayas and its implications on regional climate

Climate Dynamics - Regional climate model (RegCM-4.6.0) coupled with Community Land Model (CLM4.5), which includes SNow, ICe and Aerosol Radiation (SNICAR) model was used to investigate the effect...     

The impact of new land surface physics on the GCM simulation of climate and climate sensitivity

 Recent improvements to the Hadley Centre climate model include the introduction of a new land surface scheme called “MOSES” (Met Office Surface Exchange Scheme). MOSES is built on the previous scheme, but incorporates in addition an interactive plant photosynthesis and conductance module, and a new soil thermodynamics scheme which simulates the freezing and melting of soil water, and takes account of the dependence of soil thermal characteristics on the frozen and unfrozen components. The impact of these new features is demonstrated by comparing 1×CO₂ and 2×CO₂ climate simulations carried out using the old (UKMO) and new (MOSES) land surface schemes. MOSES is found to improve the simulation of current climate. Soil water freezing tends to warm the high-latitude land in the northern Hemisphere during autumn and winter, whilst the increased soil water availability in MOSES alleviates a spurious summer drying in the mid-latitudes. The interactive canopy conductance responds directly to CO₂, supressing transpiration as the concentration increases and producing a significant enhancement of the warming due to the radiative effects of CO₂ alone. Received: 16 March 1998 / Accepted: 4 August 1998     

Effect of storm clustering on water balance estimates and its implications for climate impact assessment

Daily and monthly-based water balance computations are made for areas with climates ranging from humid (Coshocton, Ohio) through Mediterranean (Watsonville, California) and semi-arid (Dodge City, Kansas) to arid conditions (Tucson, Arizona). Monthly procedures lead to an underestimate of observed mean annual runoff by 14% in Coshocton, 59% in Tucson, and an overestimate by 9% in Watsonville. Daily balance calculations increase model accuracy. The improvement in runoff estimates by using the daily method is most significant for arid climates. Daily-monthly departures are greater in the semi-arid and arid areas than in the humid and Mediterranean areas. In terms of mean annual runoff, the difference between monthly estimates and daily estimates is 42.5% in arid Tucson, 58.2% in semi-arid Dodge City, but only 8.9% in humid Coshocton and 5.6% in Mediterranean Watsonville. The daily-monthly departures in soil moisture estimates are generally less than 10% in the humid and Mediterranean climates, but well above 50% in most months in the arid and semi-arid climates. Regression analysis indicates the daily-monthly difference in moisture surplus estimates correlates well with the amount of storm clustering within a month. Monthly computations depart increasingly from daily computations as storm clustering increases. The hydrological impacts of changes in storm clustering are studied by forcing the water balance model with daily precipitation sequences based on hypothetical storm scenarios. Total annual moisture surplus tends to increase with increased storm clustering. In the arid and semi-arid climates, the differences between the most and least clustering scenarios equal 35% up to 60% of surplus water generated by normal storms. They are about 20% in the cases of the humid and Mediterranean climates. These results suggest future potential changes in climatic variability such as storm delivery patterns can have significant impacts on water resource availability.     

On the contribution of local feedback mechanisms to the range of climate sensitivity in two GCM ensembles

Global and local feedback analysis techniques have been applied to two ensembles of mixed layer equilibrium CO₂ doubling climate change experiments, from the CFMIP (Cloud Feedback Model Intercomparison Project) and QUMP (Quantifying Uncertainty in Model Predictions) projects. Neither of these new ensembles shows evidence of a statistically significant change in the ensemble mean or variance in global mean climate sensitivity when compared with the results from the mixed layer models quoted in the Third Assessment Report of the IPCC. Global mean feedback analysis of these two ensembles confirms the large contribution made by inter-model differences in cloud feedbacks to those in climate sensitivity in earlier studies; net cloud feedbacks are responsible for 66% of the inter-model variance in the total feedback in the CFMIP ensemble and 85% in the QUMP ensemble. The ensemble mean global feedback components are all statistically indistinguishable between the two ensembles, except for the clear-sky shortwave feedback which is stronger in the CFMIP ensemble. While ensemble variances of the shortwave cloud feedback and both clear-sky feedback terms are larger in CFMIP, there is considerable overlap in the cloud feedback ranges; QUMP spans 80% or more of the CFMIP ranges in longwave and shortwave cloud feedback. We introduce a local cloud feedback classification system which distinguishes different types of cloud feedbacks on the basis of the relative strengths of their longwave and shortwave components, and interpret these in terms of responses of different cloud types diagnosed by the International Satellite Cloud Climatology Project simulator. In the CFMIP ensemble, areas where low-top cloud changes constitute the largest cloud response are responsible for 59% of the contribution from cloud feedback to the variance in the total feedback. A similar figure is found for the QUMP ensemble. Areas of positive low cloud feedback (associated with reductions in low level cloud amount) contribute most to this figure in the CFMIP ensemble, while areas of negative cloud feedback (associated with increases in low level cloud amount and optical thickness) contribute most in QUMP. Classes associated with high-top cloud feedbacks are responsible for 33 and 20% of the cloud feedback contribution in CFMIP and QUMP, respectively, while classes where no particular cloud type stands out are responsible for 8 and 21%.     

Origins of differences in climate sensitivity, forcing and feedback in climate models

We diagnose climate feedback parameters and CO₂ forcing including rapid adjustment in twelve atmosphere/mixed-layer-ocean (“slab”) climate models from the CMIP3/CFMIP-1 project (the AR4 ensemble) and fifteen parameter-perturbed versions of the HadSM3 slab model (the PPE). In both ensembles, differences in climate feedbacks can account for approximately twice as much of the range in climate sensitivity as differences in CO₂ forcing. In the AR4 ensemble, cloud effects can explain the full range of climate sensitivities, and cloud feedback components contribute four times as much as cloud components of CO₂ forcing to the range. Non-cloud feedbacks are required to fully account for the high sensitivities of some models however. The largest contribution to the high sensitivity of HadGEM1 is from a high latitude clear-sky shortwave feedback, and clear-sky longwave feedbacks contribute substantially to the highest sensitivity members of the PPE. Differences in low latitude ocean regions (30°N/S) contribute more to the range than those in mid-latitude oceans (30–55°N/S), low/mid latitude land (55°N/S) or high latitude ocean/land (55–90°N/S), but contributions from these other regions are required to account fully for the higher model sensitivities, for example from land areas in IPSL CM4. Net cloud feedback components over the low latitude oceans sorted into percentile ranges of lower tropospheric stability (LTS) show largest differences among models in stable regions, mainly due to their shortwave components, most of which are positive in spite of increasing LTS. Differences in the mid-stability range are smaller, but cover a larger area, contributing a comparable amount to the range in climate sensitivity. These are strongly anti-correlated with changes in subsidence. Cloud components of CO₂ forcing also show the largest differences in stable regions, and are strongly anticorrelated with changes in estimated inversion strength (EIS). This is qualitatively consistent with what would be expected from observed relationships between EIS and low-level cloud fraction. We identify a number of cases where individual models show unusually strong forcings and feedbacks compared to other members of the ensemble. We encourage modelling groups to investigate unusual model behaviours further with sensitivity experiments. Most of the models fail to correctly reproduce the observed relationships between stability and cloud radiative effect in the subtropics, indicating that there remains considerable room for model improvements in the future.     

Hedging the climate sensitivity risks of a temperature target

This paper addresses the problem of meeting a predetermined temperature target cost-effectively under uncertainty and gradual learning on climate sensitivity. The firstorder optimality conditions to a stochastic cost-minimization problem with a temperature constraint are first provided, portraying how marginal costs evolve with an optimal hedging strategy. Then, numerical stochastic scenarios with cost curves fitted to recent climate changemitigation scenarios are presented, illustrating both the range of possible future pathways and the effect of uncertainty to the solution. Last, the effect of several different sets of assumptions on the optimal hedging strategy are analyzed. The results highlight that the hedging of climate sensitivity risk calls for deeper early reductions, although the possibility of different assumptions prevents providing accurate policy guidance.     

径流对气候变化的敏感性分析

全球变暖愈来愈引起社会各界的关注 ,本文利用月水文模型 ,采取假定气候方案 ,以黄河流域为例 ,分析了径流对气候变化的敏感性。结果表明 ,径流对降水变化的响应较气温变化显著 ;一般情况下 ,半干旱地区径流较半湿润地区对气候变化敏感 ,人类活动的影响可在一定程度上削弱径流对气候变化的敏感性