Changes in temperature and precipitation extremes in the IPCC ensemble of global coupled model simulations

Temperature and precipitation extremes and their potential future changes are evaluated in an ensemble of global coupled climate models participating in the Intergovernmental Panel on Climate Change (IPCC) diagnostic exercise for the Fourth Assessment Report (AR4). Climate extremes are expressed in terms of 20-yr return values of annual extremes of near-surface temperature and 24-h precipitation amounts. The simulated changes in extremes are documented for years 2046–65 and 2081–2100 relative to 1981–2000 in experiments with the Special Report on Emissions Scenarios (SRES) B1, A1B, and A2 emission scenarios. Overall, the climate models simulate present-day warm extremes reasonably well on the global scale, as compared to estimates from reanalyses. The model discrepancies in simulating cold extremes are generally larger than those for warm extremes, especially in sea ice–covered areas. Simulated present-day precipita-tion extremes are plausible in the extratropics, but uncertainties in extreme precipitation in the Tropics are very large, both in the models and the available observationally based datasets. Changes in warm extremes generally follow changes in the mean summertime temperature. Cold ex-tremes warm faster than warm extremes by about 30%–40%, globally averaged. The excessive warming of cold extremes is generally confined to regions where snow and sea ice retreat with global warming. With th

Uncertainty in the evolution of climate feedback traced to the strength of the Atlantic Meridional Overturning Circulation

In most coupled climate models, effective climate sensitivity increases for a few decades following an abrupt CO2 increase. The change in the climate feedback parameter between the first 20 years and the subsequent 130 years is highly model dependent. In this study, we suggest that the intermodel spread of changes in climate feedback can be partially traced to the evolution of the Atlantic Meridional Overturning Circulation. Models with stronger Atlantic Meridional Overturning Circulation recovery tend to project more amplified warming in the Northern Hemisphere a few decades after a quadrupling of CO2. Tropospheric stability then decreases as the Northern Hemisphere gets warmer, which leads to an increase in both the lapse‐rate and shortwave cloud feedbacks. Our results suggest that constraining future ocean circulation changes will be necessary for accurate climate sensitivity projections

A Bayesian approach for statistical–physical bulk parameterization of rain microphysics. Part II: Idealized Markov chain Monte Carlo experiments

Observationally informed development of a new framework for bulk rain microphysics, the Bayesian Observationally Constrained Statistical–Physical Scheme (BOSS; described in Part I of this study), is demonstrated. This scheme’s development is motivated by large uncertainties in cloud and weather simulations associated with approximations and assumptions in existing microphysics schemes. Here, a proof-of-concept study is presented using a Markov chain Monte Carlo sampling algorithm with BOSS to probabilistically estimate microphysical process rates and parameters directly from a set of synthetically generated rain observations. The framework utilized is an idealized steady-state one-dimensional column rainshaft model with specified column-top rain properties and a fixed thermodynamical profile. Different configurations of BOSS—flexibility being a key feature of this approach—are constrained via synthetic observations generated from a traditional three-moment bulk microphysics scheme. The ability to retrieve correct parameter values when the true parameter values are known is illustrated. For cases when there is no set of true parameter values, the accuracy of configurations of BOSS that have different levels of complexity is compared. It is found that addition of the sixth moment as a prognostic variable improves prediction of the third moment (proportional to bulk rain mass) and rain rate. In contrast, increasing process rate formulation complexity by adding more power terms has little benefit—a result that is explained using further-idealized experiments. BOSS rainshaft simulations are shown to well estimate the true process rates from constraint by bulk rain observations, with the additional benefit of rigorously quantified uncertainty of these estimates

Diagnosing ENSO and global warming tropical precipitation shifts using surface relative humidity and temperature

This is the final version of the article. Available from American Meteorological Society via the DOI in this recordLarge uncertainty remains in future projections of tropical precipitation change under global warming. A simplified method for diagnosing tropical precipitation change is tested here on present day El Niño-Southern Oscillation (ENSO) precipitation shifts. This method, based on the weak temperature gradient approximation, assumes precipitation is associated with local surface relative humidity (RH) and air temperature (SAT), relative to the tropical mean. Observed and simulated changes in RH and SAT are subsequently used to diagnose changes in precipitation. Present day ENSO precipitation shifts are successfully diagnosed using observations (r = 0:69), and an ensemble of atmosphere-only (0:51 ≤ r ≤ 0:8) and coupled (0:5 ≤ r ≤ 0:87) climate model simulations. RH (r = 0:56) is much more influential than SAT (r = 0:27) in determining ENSO precipitation shifts for observations and climate model simulations over both land and ocean. Using inter-model differences, a significant relationship is demonstrated between method performance over ocean for present day ENSO and projected global warming (r = 0:68). As a caveat, we note that mechanisms leading to ENSO-related precipitation changes are not a direct analogue for global warming-related precipitation changes. The diagnosis method presented here demonstrates plausible mechanisms which relate changes in precipitation, RH and SAT under different climate perturbations. Therefore, uncertainty in future tropical precipitation changes may be linked with uncertainty in future RH and SAT changes.AT was supported by a NERC studentship NE/M009599/1 and CASE funding from the Met Office. FHL was part supported by the UK-China Research and Innovation Partnership Fund through the Met Office Climate Science for Service Partnership (CSSP) China as part of the Newton Fund. RC was supported by the Newton Fund through the Met Office Climate Science for Service Partnership Brazil (CSSP Brazil)

Southern African summer-rainfall variability, and its teleconnections, on interannual to interdecadal timescales in CMIP5 models

23 pagesInternational audienceThis study provides the first assessment of CMIP5 model performances in simulating southern Africa (SA) rainfall variability in austral summer (Nov–Feb), and its teleconnections with large-scale climate variability at different timescales. Observed SA rainfall varies at three major timescales: interannual (2–8 years), quasi-decadal (8–13 years; QDV) and interdecadal (15–28 years; IDV). These rainfall fluctuations are, respectively, associated with El Niño Southern Oscillation (ENSO), the Interdecadal Pacific Oscillation (IPO) and the Pacific Decadal Oscillation (PDO), interacting with climate anomalies in the South Atlantic and South Indian Ocean. CMIP5 models produce their own variability, but perform better in simulating interannual rainfall variability, while QDV and IDV are largely underestimated. These limitations can be partly explained by spatial shifts in core regions of SA rainfall variability in the models. Most models reproduce the impact of La Niña on rainfall at the interannual scale in SA, in spite of limitations in the representation of ENSO. Realistic links between negative IPO are found in some models at the QDV scale, but very poor performances are found at the IDV scale. Strong limitations, i.e. loss or reversal of these teleconnections, are also noted in some simulations. Such model errors, however, do not systematically impact the skill of simulated rainfall variability. This is because biased SST variability in the South Atlantic and South Indian Oceans strongly impact model skills by modulating the impact of Pacific modes of variability. Using probabilistic multi-scale clustering, model uncertainties in SST variability are primarily driven by differences from one model to another, or comparable models (sharing similar physics), at the global scale. At the regional scale, i.e. SA rainfall variability and associated teleconnections, while differences in model physics remain a large source of uncertainty, the contribution of internal climate variability is increasing. This is particularly true at the QDV and IDV scales, where the individual simulations from the same model tend to differentiate, and the sampling error increase

Quantifying sources of inter-model diversity in the cloud albedo effect

There is large diversity in simulated aerosol forcing among models that participated in the fifth Coupled Model Intercomparison Project (CMIP5), particularly related to aerosol interactions with clouds. Here we use the reported model data and fitted aerosol-cloud relations to separate the main sources of inter-model diversity in the magnitude of the cloud albedo effect. There is large diversity in the global load and spatial distribution of sulfate aerosol, as well as in global-mean cloud-top effective radius. The use of different parameterizations of aerosol-cloud interactions makes the largest contribution to diversity in modeled radiative forcing (up to -39%, +48% about the mean estimate). Uncertainty in pre-industrial sulfate load also makes a substantial contribution (-15%, +61% about the mean estimate), with smaller contributions from inter-model differences in the historical change in sulfate load and in mean cloud fraction

An energy balance perspective on regional CO2-induced temperature changes in CMIP5 models

An energy balance decomposition of temperature changes is conducted for idealized transient CO2-only simulations in the fifth phase of the Coupled Model Intercomparison Project. The multimodel global mean warming is dominated by enhanced clear-sky greenhouse effect due to increased CO2 and water vapour, but other components of the energy balance substantially modify the geographical and seasonal patterns of the change. Changes in the net surface energy flux are important over the oceans, being especially crucial for the muted warming over the northern North Atlantic and for the seasonal cycle of warming over the Arctic Ocean. Changes in atmospheric energy flux convergence tend to smooth the gradients of temperature change and reduce its land-sea contrast, but they also amplify the seasonal cycle of warming in northern North America and Eurasia. The three most important terms for intermodel differences in warming are the changes in the clear-sky greenhouse effect, clouds, and the net surface energy flux, making the largest contribution to the standard deviation of annual mean temperature change in 34, 29 and 20 % of the world, respectively. Changes in atmospheric energy flux convergence mostly damp intermodel variations of temperature change especially over the oceans. However, the opposite is true for example in Greenland and Antarctica, where the warming appears to be substantially controlled by heat transport from the surrounding sea areas.Peer reviewe

Pattern scaling using ClimGen: monthly-resolution future climate scenarios including changes in the variability of precipitation

Development, testing and example applications of the pattern-scaling approach for generating future climate change projections are reported here, with a focus on a particular software application called “ClimGen”. A number of innovations have been implemented, including using exponential and logistic functions of global-mean temperature to represent changes in local precipitation and cloud cover, and interpolation from climate model grids to a finer grid while taking into account land-sea contrasts in the climate change patterns. Of particular significance is a new approach for incorporating changes in the inter-annual variability of monthly precipitation simulated by climate models. This is achieved by diagnosing simulated changes in the shape of the gamma distribution of monthly precipitation totals, applying the pattern-scaling approach to estimate changes in the shape parameter under a future scenario, and then perturbing sequences of observed precipitation anomalies so that their distribution changes according to the projected change in the shape parameter. The approach cannot represent changes to the structure of climate timeseries (e.g. changed autocorrelation or teleconnection patterns) were they to occur, but is shown here to be more successful at representing changes in low precipitation extremes than previous pattern-scaling methods