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A. Slingo

Abstract

A new parameterization is presented for the shortwave radiative properties of water clouds, which is fast enough to be included in general circulation models (GCMs). It employs the simple relationships found by Slingo and Schrecker for the optical depth, single scatter albedo and asymmetry parameter of cloud drops as function of the cloud liquid water path and equivalent radius of the drop size distribution. The cloud radiative properties are then obtained from standard two-stream equations for a homogeneous layer. The effect of water vapor absorption within the cloud is ignored in this version, leading to a small underestimate of the cloud absorption. The parameterization is compared with other schemes and with aircraft observations. It performs satisfactorily even when only four spectral bands are employed. The explicit separation of the dependence of the derived cloud radiative properties on the liquid water path and equivalent radius is new, and should prove valuable for climate change investigations.

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Mark. A. Miller and Anthony Slingo

The Atmospheric Radiation Measurement (ARM) Mobile Facility (AMF) was recently developed to enable collection of detailed climate data in locations not currently sampled by ARM's five fixed sites. The AMF includes a comprehensive suite of active and passive remote sensors, including cloud radar, that sample the atmosphere in a narrow column above its location. Surface radiation, aerosols, and fluxes are also measured and there is an ancillary measurement facility to help quantify local gradients. The AMF is deployed at no cost to the principal investigator or institution for periods from six months to one year on the basis of an international proposal competition judged by a nonpartisan board. The proposal to ARM that led to the initial international deployment of the AMF in Niamey, Niger, was titled “Radiative Atmospheric Divergence Using the AMF, GERB Data, and AMMA Stations (RADAGAST).” This paper provides a description of the instruments that compose the AMF, its charter, a description of its deployment in support of RADAGAST, and examples of data that have been collected in Africa.

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Richard P. Allan, A. Slingo, and M. A. Ringer

Abstract

Satellite measurements of the radiation budget and data from the U.S. National Centers for Environmental Prediction–National Center for Atmospheric Research reanalysis are used to investigate the links between anomalous cloud radiative forcing over the tropical west Pacific warm pool and the tropical dynamics and sea surface temperature (SST) distribution during 1998. The ratio, N, of the shortwave cloud forcing (SWCF) to longwave cloud forcing (LWCF) (N = −SWCF/LWCF) is used to infer information on cloud altitude. A higher than average N during 1998 appears to be related to two separate phenomena. First, dynamic regime-dependent changes explain high values of N (associated with low cloud altitude) for small magnitudes of SWCF and LWCF (low cloud fraction), which reflect the unusual occurrence of mean subsiding motion over the tropical west Pacific during 1998, associated with the anomalous SST distribution. Second, Tropics-wide long-term changes in the spatial-mean cloud forcing, independent of dynamic regime, explain the higher values of N during both 1998 and in 1994/95. The changes in dynamic regime and their anomalous structure in 1998 are well simulated by version HadAM3 of the Hadley Centre climate model, forced by the observed SSTs. However, the LWCF and SWCF are poorly simulated, as are the interannual changes in N. It is argued that improved representation of LWCF and SWCF and their dependence on dynamical forcing are required before the cloud feedbacks simulated by climate models can be trusted.

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A. Slingo, J. A. Pamment, and M. J. Webb

Abstract

Results are presented from the project Clear-sky Longwave from ERA (CLERA), in which simulations were performed of clear-sky longwave fluxes and heating rates for the period 1979–93, based on data from the European Centre for Medium-Range Weather Forecasts Re-Analysis project (ERA). This paper presents selected results from CLERA and compares the clear-sky outgoing longwave radiation (OLR) with data from the Earth Radiation Budget Experiment (ERBE). Over much of the globe, especially over the oceans, the clear-sky OLR from CLERA is within the expected uncertainty in the ERBE data of ±5 W m−2. Elsewhere, there are larger differences and to study these the ERA data are compared with independent sources of information: surface synoptic observations of screen-level temperatures and retrievals of the total column moisture and upper-tropospheric humidity from satellite data. Over land, the largest clear-sky OLR differences occur at high latitudes in winter and these can be explained by the fact that the ERA surface temperatures are too low in these regions. However, for many other regions over land there was no obvious explanation for the clear-sky OLR differences. Over the oceans, the clear-sky OLR differences in the Tropics are consistent with known systematic biases in ERBE, the most important consequence of which is that the ERBE clear-sky OLR is too high in convective regions such as the ITCZ.

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A. Slingo, R. C. Wilderspin, and S. J. Brentnall

Abstract

Results are presented from an integration of the U.K. Meteorological Office 11-Layer Atmospheric General Circulation Model, with emphasis on the simulation of the diurnal cycle of the outgoing longwave radiation. The model reproduces many of the feature which have been noted from observational studies with satellite data. It is argued that such comparisons between models and observations have considerable potential not only for validating the cloud and other parameterization schemes used in models but also for understanding the origin of the observed variations.

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M. J. Woodage, A. Slingo, S. Woodward, and R. E. Comer

Abstract

The atmospheric component of the United Kingdom’s new High-resolution Global Environmental Model (HiGEM) has been run with interactive aerosol schemes that include biomass burning and mineral dust. Dust emission, transport, and deposition are parameterized within the model using six particle size divisions, which are treated independently. The biomass is modeled in three nonindependent modes, and emissions are prescribed from an external dataset. The model is shown to produce realistic horizontal and vertical distributions of these aerosols for each season when compared with available satellite- and ground-based observations and with other models. Combined aerosol optical depths off the coast of North Africa exceed 0.5 both in boreal winter, when biomass is the main contributor, and also in summer, when the dust dominates. The model is capable of resolving smaller-scale features, such as dust storms emanating from the Bodélé and Saharan regions of North Africa and the wintertime Bodélé low-level jet. This is illustrated by February and July case studies, in which the diurnal cycles of model variables in relation to dust emission and transport are examined. The top-of-atmosphere annual mean radiative forcing of the dust is calculated and found to be globally quite small but locally very large, exceeding 20 W m−2 over the Sahara, where inclusion of dust aerosol is shown to improve the model radiative balance. This work extends previous aerosol studies by combining complexity with increased global resolution and represents a step toward the next generation of models to investigate aerosol–climate interactions.

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V. D. Pope, J. A. Pamment, D. R. Jackson, and A. Slingo

Abstract

Simulations of the Hadley Centre Atmospheric Climate Model version 3, HadAM3, are used to investigate the impact of increasing vertical resolution on simulated climates. In particular, improvements in the representation of water vapor and temperature in the upper troposphere and lower stratosphere are identified with more accurate advection. Degradations in some aspects of the simulation in the Tropics are identified with undesirable resolution dependencies in the physical parameterizations. The overall improvements in the water vapor and temperature distribution lead to improvements in the clear-sky longwave radiative fluxes with higher vertical resolution.

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A. Slingo, J. A. Pamment, R. P. Allan, and P. S. Wilson

Abstract

Many studies have been made of the water vapor feedback, in both satellite data and climate model simulations. Most infer the magnitude of the feedback from the variability present in geographical distributions of the key variables, or from their seasonal variations, often using data only over the oceans. It is argued that a more direct measure of the feedback should come from the interannual variability of global mean quantities, because this timescale and space scale is more appropriate for such a global phenomenon. To investigate this suggestion, the feedback derived from the simulations of clear-sky longwave fluxes (CLERA), which used data from the 15-yr reanalysis project of the European Centre for Medium-Range Weather Forecasts, is compared with simulations by the latest version of the Hadley Centre climate model. Results are taken from an integration of the atmosphere-only version of the climate model with prescribed sea surface temperatures, as well as from a control and a global warming simulation by the coupled ocean–atmosphere version. There is broad consistency between the results from CLERA and the climate model as to the strength of the feedback, although there is considerable scatter in the CLERA results. The signal of changes in the well-mixed greenhouse gases is weak in CLERA but is dominant in the global warming simulation and has to be removed in order to diagnose the water vapor feedback. This result has implications for the exploitation of long time series of satellite and other data to study this and other feedbacks.

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A. J. Challinor, J. M. Slingo, T. R. Wheeler, P. Q. Craufurd, and D. I. F. Grimes

Abstract

A methodology is presented for the development of a combined seasonal weather and crop productivity forecasting system. The first stage of the methodology is the determination of the spatial scale(s) on which the system could operate; this determination has been made for the case of groundnut production in India. Rainfall is a dominant climatic determinant of groundnut yield in India. The relationship between yield and rainfall has been explored using data from 1966 to 1995. On the all-India scale, seasonal rainfall explains 52% of the variance in yield. On the subdivisional scale, correlations vary between variance r 2 = 0.62 (significance level p < 10–4) and a negative correlation with r 2 = 0.1 (p = 0.13). The spatial structure of the relationship between rainfall and groundnut yield has been explored using empirical orthogonal function (EOF) analysis. A coherent, large-scale pattern emerges for both rainfall and yield. On the subdivisional scale (∼300 km), the first principal component (PC) of rainfall is correlated well with the first PC of yield (r 2 = 0.53, p < 10–4), demonstrating that the large-scale patterns picked out by the EOFs are related. The physical significance of this result is demonstrated. Use of larger averaging areas for the EOF analysis resulted in lower and (over time) less robust correlations. Because of this loss of detail when using larger spatial scales, the subdivisional scale is suggested as an upper limit on the spatial scale for the proposed forecasting system. Further, district-level EOFs of the yield data demonstrate the validity of upscaling these data to the subdivisional scale. Similar patterns have been produced using data on both of these scales, and the first PCs are very highly correlated (r 2 = 0.96). Hence, a working spatial scale has been identified, typical of that used in seasonal weather forecasting, that can form the basis of crop modeling work for the case of groundnut production in India. Last, the change in correlation between yield and seasonal rainfall during the study period has been examined using seasonal totals and monthly EOFs. A further link between yield and subseasonal variability is demonstrated via analysis of dynamical data.

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A. J. Challinor, T. R. Wheeler, J. M. Slingo, P. Q. Craufurd, and D. I. F. Grimes

Abstract

Reanalysis data provide an excellent test bed for impacts prediction systems, because they represent an upper limit on the skill of climate models. Indian groundnut (Arachis hypogaea L.) yields have been simulated using the General Large-Area Model (GLAM) for annual crops and the European Centre for Medium-Range Weather Forecasts (ECMWF) 40-yr reanalysis (ERA-40). The ability of ERA-40 to represent the Indian summer monsoon has been examined. The ability of GLAM, when driven with daily ERA-40 data, to model both observed yields and observed relationships between subseasonal weather and yield has been assessed. Mean yields were simulated well across much of India. Correlations between observed and modeled yields, where these are significant, are comparable to correlations between observed yields and ERA-40 rainfall. Uncertainties due to the input planting window, crop duration, and weather data have been examined. A reduction in the root-mean-square error of simulated yields was achieved by applying bias correction techniques to the precipitation. The stability of the relationship between weather and yield over time has been examined. Weather–yield correlations vary on decadal time scales, and this has direct implications for the accuracy of yield simulations. Analysis of the skewness of both detrended yields and precipitation suggest that nonclimatic factors are partly responsible for this nonstationarity. Evidence from other studies, including data on cereal and pulse yields, indicates that this result is not particular to groundnut yield. The detection and modeling of nonstationary weather–yield relationships emerges from this study as an important part of the process of understanding and predicting the impacts of climate variability and change on crop yields.

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