Past, present and future precipitation in the Middle East: insights from models and observations

Anthropogenic changes in precipitation pose a serious threat to society—particularly in regions such as the Middle East that already face serious water shortages. However, climate model projections of regional precipitation remain highly uncertain. Moreover, standard resolution climate models have particular difficulty representing precipitation in the Middle East, which is modulated by complex topography, inland water bodies and proximity to the Mediterranean Sea. Here we compare precipitation changes over the twenty-first century against both millennial variability during the Holocene and interannual variability in the present day. In order to assess the climate model and to make consistent comparisons, this study uses new regional climate model simulations of the past, present and future in conjunction with proxy and historical observations. We show that the pattern of precipitation change within Europe and the Middle East projected by the end of the twenty-first century has some similarities to that which occurred during the Holocene. In both cases, a poleward shift of the North Atlantic storm track and a weakening of the Mediterranean storm track appear to cause decreased winter rainfall in southern Europe and the Middle East and increased rainfall further north. In contrast, on an interannual time scale, anomalously dry seasons in the Middle East are associated with a strengthening and focusing of the storm track in the north Mediterranean and hence wet conditions throughout southern Europe.

Ice-sheet contributions to future sea-level change

Accurate simulation of ice-sheet surface mass balance requires higher spatial resolution than is afforded by typical atmosphere-ocean general circulation models (AOGCMs), owing, in particular, to the need to resolve the narrow and steep margins where the majority of precipitation and ablation occurs. We have developed a method for calculating mass-balance changes by combining ice-sheet average time-series from AOGCM projections for future centuries, both with information from high-resolution climate models run for short periods and with a 20 km ice-sheet mass-balance model. Antarctica contributes negatively to sea level on account of increased accumulation, while Greenland contributes positively because ablation increases more rapidly. The uncertainty in the results is about 20% for Antarctica and 35% for Greenland. Changes in ice-sheet topography and dynamics are not included, but we discuss their possible effects. For an annual- and area-average warming exceeding 4.5 +/- 0.9 K in Greenland and 3.1 +/- 0.8 K in the global average, the net surface mass balance of the Greenland ice sheet becomes negative, in which case it is likely that the ice sheet would eventually be eliminated, raising global-average sea level by 7 m.

Radiative forcing since preindustrial times due to ozone change in the troposphere and the lower stratosphere

Changes in atmospheric ozone have occurred since the preindustrial era as a result of increasing anthropogenic emissions. Within ACCENT, a European Network of Excellence, ozone changes between 1850 and 2000 are assessed for the troposphere and the lower stratosphere ( up to 30 km) by a variety of seven chemistry-climate models and three chemical transport models. The modeled ozone changes are taken as input for detailed calculations of radiative forcing. When only changes in chemistry are considered ( constant climate) the modeled global-mean tropospheric ozone column increase since preindustrial times ranges from 7.9 DU to 13.8 DU among the ten participating models, while the stratospheric column reduction lies between 14.1 DU and 28.6 DU in the models considering stratospheric chemistry. The resulting radiative forcing is strongly dependent on the location and altitude of the modeled ozone change and varies between 0.25 Wm(-2) and 0.45 Wm(-2) due to ozone change in the troposphere and - 0.123 Wm(-2) and + 0.066 Wm(-2) due to the stratospheric ozone change. Changes in ozone and other greenhouse gases since preindustrial times have altered climate. Six out of the ten participating models have performed an additional calculation taking into account both chemical and climate change. In most models the isolated effect of climate change is an enhancement of the tropospheric ozone column increase, while the stratospheric reduction becomes slightly less severe. In the three climate-chemistry models with detailed tropospheric and stratospheric chemistry the inclusion of climate change increases the resulting radiative forcing due to tropospheric ozone change by up to 0.10 Wm(-2), while the radiative forcing due to stratospheric ozone change is reduced by up to 0.034 Wm(-2). Considering tropospheric and stratospheric change combined, the total ozone column change is negative while the resulting net radiative forcing is positive.

The climate sensitivity and its components diagnosed from Earth Radiation Budget data

One of the major uncertainties in the ability to predict future climate change, and hence its impacts, is the lack of knowledge of the earth's climate sensitivity. Here, data are combined from the 1985-96 Earth Radiation Budget Experiment (ERBE) with surface temperature change information and estimates of radiative forcing to diagnose the climate sensitivity. Importantly, the estimate is completely independent of climate model results. A climate feedback parameter of 2.3 +/- 1.4 W m(-2) K-1 is found. This corresponds to a 1.0-4.1-K range for the equilibrium warming due to a doubling of carbon dioxide (assuming Gaussian errors in observable parameters, which is approximately equivalent to a uniform "prior" in feedback parameter). The uncertainty range is due to a combination of the short time period for the analysis as well as uncertainties in the surface temperature time series and radiative forcing time series, mostly the former. Radiative forcings may not all be fully accounted for; however, all argument is presented that the estimate of climate sensitivity is still likely to be representative of longer-term climate change. The methodology can be used to 1) retrieve shortwave and longwave components of climate feedback and 2) suggest clear-sky and cloud feedback terms. There is preliminary evidence of a neutral or even negative longwave feedback in the observations, suggesting that current climate models may not be representing some processes correctly if they give a net positive longwave feedback.

Current changes in tropical precipitation

Current changes in tropical precipitation from satellite data and climate models are assessed. Wet and dry regions of the tropics are defined as the highest 30% and lowest 70% of monthly precipitation values. Observed tropical ocean trends in the wet regime (1.8%/decade) and the dry regions (−2.6%/decade) according to the Global Precipitation Climatology Project (GPCP) over the period including Special Sensor Microwave Imager (SSM/I) data (1988–2008), where GPCP is believed to be more reliable, are of smaller magnitude than when including the entire time series (1979–2008) and closer to model simulations than previous comparisons. Analysing changes in extreme precipitation using daily data within the wet regions, an increase in the frequency of the heaviest 6% of events with warming for the SSM/I observations and model ensemble mean is identified. The SSM/I data indicate an increased frequency of the heaviest events with warming, several times larger than the expected Clausius–Clapeyron scaling and at the upper limit of the substantial range in responses in the model simulations.

Top-down solar modulation of climate: evidence for centennial-scale change

During the descent into the recent ‘exceptionally’ low solar minimum, observations have revealed a larger change in solar UV emissions than seen at the same phase of previous solar cycles. This is particularly true at wavelengths responsible for stratospheric ozone production and heating. This implies that ‘top-down’ solar modulation could be a larger factor in long-term tropospheric change than previously believed, many climate models allowing only for the ‘bottom-up’ effect of the less-variable visible and infrared solar emissions. We present evidence for long-term drift in solar UV irradiance, which is not found in its commonly used proxies. In addition, we find that both stratospheric and tropospheric winds and temperatures show stronger regional variations with those solar indices that do show long-term trends. A top-down climate effect that shows long-term drift (and may also be out of phase with the bottom-up solar forcing) would change the spatial response patterns and would mean that climate-chemistry models that have sufficient resolution in the stratosphere would become very important for making accurate regional/seasonal climate predictions. Our results also provide a potential explanation of persistent palaeoclimate results showing solar influence on regional or local climate indicators.

Solar influences on climate

Understanding the influence of solar variability on the Earth’s climate requires knowledge of solar variability, solar-terrestrial interactions and the mechanisms determining the response of the Earth’s climate system. We provide a summary of our current understanding in each of these three areas. Observations and mechanisms for the Sun's variability are described, including solar irradiance variations on both decadal and centennial timescales and their relation to galactic cosmic rays. Corresponding observations of variations of the Earth’s climate on associated timescales are described, including variations in ozone, temperatures, winds, clouds, precipitation and regional modes of variability such as the monsoons and the North Atlantic Oscillation. A discussion of the available solar and climate proxies is provided. Mechanisms proposed to explain these climate observations are described, including the effects of variations in solar irradiance and of charged particles. Finally, the contribution of solar variations to recent observations of global climate change are discussed.

Stray light analysis of SALEX instrument

Discrepancies between recent global earth albedo anomaly data obtained from the climate models, space and ground observations call for a new and better earth reflectance measurement technique. The SALEX (Space Ashen Light Explorer) instrument is a space-based visible and IR instrument for precise estimation of the global earth albedo by measuring the ashen light reflected off the shadowy side of the Moon from the low earth orbit. The instrument consists of a conventional 2-mirror telescope, a pair of a 3-mirror visible imager and an IR bolometer. The performance of this unique multi-channel optical system is sensitive to the stray light contamination due to the complex optical train incorporating several reflecting and refracting elements, associated mounts and the payload mechanical enclosure. This could be further aggravated by the very bright and extended observation target (i.e. the Moon). In this paper, we report the details of extensive stray light analysis including ghosts and cross-talks, leading to the optimum set of stray light precautions for the highest signal-to-noise ratio attainable.

Multi-model assessment of stratospheric ozone return dates and ozone recovery in CCMVal-2 models

Projections of stratospheric ozone from a suite of chemistry-climate models (CCMs) have been analyzed. In addition to a reference simulation where anthropogenic halogenated ozone depleting substances (ODSs) and greenhouse gases (GHGs) vary with time, sensitivity simulations with either ODS or GHG concentrations fixed at 1960 levels were performed to disaggregate the drivers of projected ozone changes. These simulations were also used to assess the two distinct milestones of ozone returning to historical values (ozone return dates) and ozone no longer being influenced by ODSs (full ozone recovery). The date of ozone returning to historical values does not indicate complete recovery from ODSs in most cases, because GHG-induced changes accelerate or decelerate ozone changes in many regions. In the upper stratosphere where CO2-induced stratospheric cooling increases ozone, full ozone recovery is projected to not likely have occurred by 2100 even though ozone returns to its 1980 or even 1960 levels well before (~2025 and 2040, respectively). In contrast, in the tropical lower stratosphere ozone decreases continuously from 1960 to 2100 due to projected increases in tropical upwelling, while by around 2040 it is already very likely that full recovery from the effects of ODSs has occurred, although ODS concentrations are still elevated by this date. In the midlatitude lower stratosphere the evolution differs from that in the tropics, and rather than a steady decrease in ozone, first a decrease in ozone is simulated from 1960 to 2000, which is then followed by a steady increase through the 21st century. Ozone in the midlatitude lower stratosphere returns to 1980 levels by ~2045 in the Northern Hemisphere (NH) and by ~2055 in the Southern Hemisphere (SH), and full ozone recovery is likely reached by 2100 in both hemispheres. Overall, in all regions except the tropical lower stratosphere, full ozone recovery from ODSs occurs significantly later than the return of total column ozone to its 1980 level. The latest return of total column ozone is projected to occur over Antarctica (~2045–2060) whereas it is not likely that full ozone recovery is reached by the end of the 21st century in this region. Arctic total column ozone is projected to return to 1980 levels well before polar stratospheric halogen loading does so (~2025–2030 for total column ozone, cf. 2050–2070 for Cly+60×Bry) and it is likely that full recovery of total column ozone from the effects of ODSs has occurred by ~2035. In contrast to the Antarctic, by 2100 Arctic total column ozone is projected to be above 1960 levels, but not in the fixed GHG simulation, indicating that climate change plays a significant role.

Dynamical influences on European climate: an uncertain future

Climate science is coming under increasing pressure to deliver projections of future climate change at spatial scales as small as a few kilometres for use in impacts studies. But is our understanding and modelling of the climate system advanced enough to offer such predictions? Here we focus on the Atlantic–European sector, and on the effects of greenhouse gas forcing on the atmospheric and, to a lesser extent, oceanic circulations. We review the dynamical processes which shape European climate and then consider how each of these leads to uncertainty in the future climate. European climate is unique in many regards, and as such it poses a unique challenge for climate prediction. Future European climate must be considered particularly uncertain because (i) the spread between the predictions of current climate models is still considerable and (ii) Europe is particularly strongly affected by several processes which are known to be poorly represented in current models.

Design and optimisation of a large-area process-based model for annual crops

The formulation of a new process-based crop model, the general large-area model (GLAM) for annual crops is presented. The model has been designed to operate on spatial scales commensurate with those of global and regional climate models. It aims to simulate the impact of climate on crop yield. Procedures for model parameter determination and optimisation are described, and demonstrated for the prediction of groundnut (i.e. peanut; Arachis hypogaea L.) yields across India for the period 1966-1989. Optimal parameters (e.g. extinction coefficient, transpiration efficiency, rate of change of harvest index) were stable over space and time, provided the estimate of the yield technology trend was based on the full 24-year period. The model has two location-specific parameters, the planting date, and the yield gap parameter. The latter varies spatially and is determined by calibration. The optimal value varies slightly when different input data are used. The model was tested using a historical data set on a 2.5degrees x 2.5degrees grid to simulate yields. Three sites are examined in detail-grid cells from Gujarat in the west, Andhra Pradesh towards the south, and Uttar Pradesh in the north. Agreement between observed and modelled yield was variable, with correlation coefficients of 0.74, 0.42 and 0, respectively. Skill was highest where the climate signal was greatest, and correlations were comparable to or greater than correlations with seasonal mean rainfall. Yields from all 35 cells were aggregated to simulate all-India yield. The correlation coefficient between observed and simulated yields was 0.76, and the root mean square error was 8.4% of the mean yield. The model can be easily extended to any annual crop for the investigation of the impacts of climate variability (or change) on crop yield over large areas. (C) 2004 Elsevier B.V. All rights reserved.

Translating climate forecasts into agricultural terms: advances and challenges

Seasonal climate prediction offers the potential to anticipate variations in crop production early enough to adjust critical decisions. Until recently, interest in exploiting seasonal forecasts from dynamic climate models (e.g. general circulation models, GCMs) for applications that involve crop simulation models has been hampered by the difference in spatial and temporal scale of GCMs and crop models, and by the dynamic, nonlinear relationship between meteorological variables and crop response. Although GCMs simulate the atmosphere on a sub-daily time step, their coarse spatial resolution and resulting distortion of day-to-day variability limits the use of their daily output. Crop models have used daily GCM output with some success by either calibrating simulated yields or correcting the daily rainfall output of the GCM to approximate the statistical properties of historic observations. Stochastic weather generators are used to disaggregate seasonal forecasts either by adjusting input parameters in a manner that captures the predictable components of climate, or by constraining synthetic weather sequences to match predicted values. Predicting crop yields, simulated with historic weather data, as a statistical function of seasonal climatic predictors, eliminates the need for daily weather data conditioned on the forecast, but must often address poor statistical properties of the crop-climate relationship. Most of the work on using crop simulation with seasonal climate forecasts has employed historic analogs based on categorical ENSO indices. Other methods based on classification of predictors or weather types can provide daily weather inputs to crop models conditioned on forecasts. Advances in climate-based crop forecasting in the coming decade are likely to include more robust evaluation of the methods reviewed here, dynamically embedding crop models within climate models to account for crop influence on regional climate, enhanced use of remote sensing, and research in the emerging area of 'weather within climate'.

Translating climate forecasts into agricultural terms: advances and challenges

Seasonal climate prediction offers the potential to anticipate variations in crop production early enough to adjust critical decisions. Until recently, interest in exploiting seasonal forecasts from dynamic climate models (e.g. general circulation models, GCMs) for applications that involve crop simulation models has been hampered by the difference in spatial and temporal scale of GCMs and crop models, and by the dynamic, nonlinear relationship between meteorological variables and crop response. Although GCMs simulate the atmosphere on a sub-daily time step, their coarse spatial resolution and resulting distortion of day-to-day variability limits the use of their daily output. Crop models have used daily GCM output with some success by either calibrating simulated yields or correcting the daily rainfall output of the GCM to approximate the statistical properties of historic observations. Stochastic weather generators are used to disaggregate seasonal forecasts either by adjusting input parameters in a manner that captures the predictable components of climate, or by constraining synthetic weather sequences to match predicted values. Predicting crop yields, simulated with historic weather data, as a statistical function of seasonal climatic predictors, eliminates the need for daily weather data conditioned on the forecast, but must often address poor statistical properties of the crop-climate relationship. Most of the work on using crop simulation with seasonal climate forecasts has employed historic analogs based on categorical ENSO indices. Other methods based on classification of predictors or weather types can provide daily weather inputs to crop models conditioned on forecasts. Advances in climate-based crop forecasting in the coming decade are likely to include more robust evaluation of the methods reviewed here, dynamically embedding crop models within climate models to account for crop influence on regional climate, enhanced use of remote sensing, and research in the emerging area of 'weather within climate'.

Effective livelihood adaptation to climate change disturbance: scale dimensions of practice in Mozambique

Natural resource-dependent societies in developing countries are facing increased pressures linked to global climate change. While social-ecological systems evolve to accommodate variability, there is growing evidence that changes in drought, storm and flood extremes are increasing exposure of currently vulnerable populations. In many countries in Africa, these pressures are compounded by disruption to institutions and variability in livelihoods and income. The interactions of both rapid and slow onset livelihood disturbance contribute to enduring poverty and slow processes of rural livelihood renewal across a complex landscape. We explore cross-scale dynamics in coping and adaptation response, drawing on qualitative data from a case study in Mozambique. The research characterises the engagements across multiple institutional scales and the types of agents involved, providing insight into emergent conditions for adaptation to climate change in rural economies, The analysis explores local responses to climate shocks, food security and poverty reduction, through informal institutions, forms of livelihood diversification and collective land-use systems that allow reciprocity, flexibility and the ability to buffer shocks. However, the analysis shows that agricultural initiatives have helped to facilitate effective livelihood renewal, through the reorganisation of social institutions and opportunities for communication, innovation and micro-credit. Although there are challenges to mainstreaming adaptation at different scales, this research shows why it is critical to assess how policies can protect conditions for emergence of livelihood transformation. (C) 2008 Elsevier Ltd. All rights reserved.

Influence of vegetation on the local climate and hydrology in the tropics: sensitivity to soil parameters

Land use change with accompanying major modifications to the vegetation cover is widespread in the tropics, due to increasing demands for agricultural land, and may have significant impacts on the climate. This study investigates (1) the influence of vegetation on the local climate in the tropics; (2) how that influence varies from region to region; and (3) how the sensitivity of the local climate to vegetation, and hence land use change, depends on the hydraulic characteristics of the soil. A series of idealised experiments with the Hadley Centre atmospheric model, HadAM3, are described in which the influence of vegetation in the tropics is assessed by comparing the results of integrations with and without tropical vegetation. The sensitivity of the results to the soil characteristics is then explored by repeating the experiments with a differing, but equally valid, description of soil hydraulic parameters. The results have shown that vegetation has a significant moderating effect on the climate throughout the tropics by cooling the surface through enhanced latent heat fluxes. The influence of vegetation is, however, seasonally dependent, with much greater impacts during the dry season when the availability of surface moisture is limited. Furthermore, there are significant regional variations both in terms of the magnitude of the cooling and in the response of the precipitation. Not all regions show a feedback of vegetation on the local precipitation; this result has been related both to vegetation type and to the prevailing meteorological conditions. An important finding has been the sensitivity of the results to the specification of the soil hydraulic parameters. The introduction of more freely draining soils has changed the soil-moisture contents of the control, vegetated system and has reduced, significantly, the climate sensitivity to vegetation and by implication, land use change. Changes to the soil parameters have also had an impact on the soil hydrology and its interaction with vegetation, by altering the partitioning between fast and slow runoff processes. These results raise important questions about the representation of highly heterogeneous soil characteristics in climate models, as well as the potential influence of land use change on the soil characteristics themselves.