Climate Forecast Models Research Articles

Use of Bayesian Merging Techniques in a Multimodel Seasonal Hydrologic Ensemble Prediction System for the Eastern United States

Abstract Skillful seasonal hydrologic predictions are useful in managing water resources, preparing for droughts and their impacts, energy planning, and many other related sectors. In this study, a seasonal hydrologic ensemble prediction system is developed and evaluated over the eastern United States, with a focus on the Ohio River basin. The system uses a hydrologic model (i.e., the Variable Infiltration Capacity model) as the central element for producing ensemble predictions of soil moisture, snow, and streamflow with lead times up to six months. One unique feature of this system is in the method for generating ensemble atmospheric forcings for the forecast period. It merges seasonal climate forecasts from multiple climate models with observed climatology in a Bayesian framework, such that the uncertainties related to the atmospheric forcings can be better quantified while the signals from individual models are combined. Simultaneously, climate model forecasts are downscaled to an appropriate spatial scale for hydrologic predictions. When generating daily meteorological forcing, the system uses the rank structures of selected historical forcing records to ensure reasonable weather patterns in space and time. Seasonal hydrologic predictions are made with this system, using seasonal climate forecast from the NCEP Climate Forecast System (CFS), and from a combination of the NCEP CFS and seven climate models in the European Union’s Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (CFS+DEMETER). Forecasts of these two types are made for the summer periods (May to October) of 1981–99 and are compared to forecasts produced with the traditional Ensemble Streamflow Prediction (ESP) approach used in operational seasonal streamflow predictions. The forecasts from this system for the summer of 1988 show very promising skill in precipitation, soil moisture, and streamflow over the Ohio River basin, especially the multimodel CFS+DEMETER forecast. The evaluation with all 19 summer forecasts shows that the multimodel CFS+DEMETER forecast is significantly better than the ESP forecast during the first two months of the forecasts. The advantage is marginal to moderate when only the CFS forecast is used. This study validates the approach of using seasonal climate predictions from dynamic climate models in hydrological predictions, and it also emphasizes the need for international collaborations to develop multimodel seasonal predictions.

A Model Study of Gravity Waves over Hurricane Humberto (2001)

Atmospheric gravity waves are known to influence global circulations. Understanding these waves and their sources help to develop parameterizations that include their effects in climate and weather forecasting models. Deep convection is believed to be a major source for these waves and hurricanes may be particularly intense sources. Simulations of Hurricane Humberto (2001) are studied using the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5). Humberto is simulated at both tropical storm and hurricane stages. Fourier transform and wavelet analysis are employed to investigate wave characteristics and their behavior in the lower stratosphere. The Fourier analysis gives a regional view of storm affects, whereas wavelet analysis gives a local picture of isolated events. Analysis of the movement of convective sources and local winds gives further insight into the mechanisms that can cause gravity waves. Convectively generated gravity waves are observed in the lower stratosphere of this model with horizontal scales of 15–300 km, vertical scales of 4–8 km, and intrinsic periods of approximately 20–100 min.

Forecasting water allocations for Bundaberg sugarcane farmers

Limited water availability in dry cane growing regions poses a challenge to sugarcane farmers. Water allocations tend to be lower at the beginning of the water season, and are increased during the season when inflows are captured. Probabilistic information reflecting the likelihood of specified increases in water allocation is not available to sugarcane farmers. The present paper describes how seasonal climate forecasts were used to provide this information for the 2001/2002 season as part of a case study involving sugarcane farmers in Bundaberg, Australia. Water allocation forecasts were then supplied to an irrigation simulation scheduling system to provide guidance about when and how much water could be applied. This research was underpinned by a cross-institutional collaboration that engaged industry, extension officers, engineers from the water authority and scientists from agriculture and climatology. The key findings from this investigation were 2-fold: the participatory approach (1) contributed to the development of information needed by industry, and (2) demonstrated the potential usefulness of climate forecasting models, hydrological models and cropping system simulators to contribute to enhancing knowledge about water availability and application. Additional investigations are required before this technology can be operationalised.

A high‐order arbitrarily unstructured finite‐volume model of the global atmosphere: Tests solving the shallow‐water equations

AbstractSimulations of the global atmosphere for weather and climate forecasting require fast and accurate solutions and so operational models use high‐order finite differences on regular structured grids. This precludes the use of local refinement; techniques allowing local refinement are either expensive (e.g. high‐order finite element techniques) or have reduced accuracy at changes in resolution (e.g. unstructured finite volume with linear differencing).We present solutions of the shallow‐water equations for westerly flow over a mid‐latitude mountain from a finite‐volume model written using OpenFOAM. A second‐/third‐order accurate differencing scheme is applied on arbitrarily unstructured meshes made up of various shapes and refinement patterns. The results are as accurate as equivalent resolution spectral methods. Using lower‐order differencing reduces accuracy at a refinement pattern which allows errors from refinement of the mountain to accumulate and reduces the global accuracy over a 15‐day simulation. We have, therefore, introduced a scheme that fits a 2D cubic polynomial approximately on a stencil around each cell. Using this scheme means that refinement of the mountain improves the accuracy after a 15‐day simulation.This is a more severe test of local mesh refinement for global simulations than has been presented but a realistic test if these techniques are to be used operationally. These efficient, high‐order, schemes may make it possible for local mesh refinement to be used by weather and climate forecast models. Copyright © 2007 John Wiley & Sons, Ltd.

Downscaling rainfall temporal variability

A realistic description of land surface/atmosphere interactions in climate and hydrologic studies requires the specification of the rainfall forcing at aggregation scales of 1 hour or less. This is in contrast with the wide availability of daily rainfall observations and with the typically coarse output resolution of climate and numerical weather forecast models. Several methods have been devised to generate hourly or subhourly data from daily or monthly values, which usually rely on statistical regressions determined under the current climate conditions. Here we present a new method for downscaling rainfall in time using theoretically based estimates of rainfall variability at the hourly scale from daily statistics. The method is validated on a wide data set representative of different rainfall regimes and produces approximately unbiased estimates of rainfall variance at the hourly scale when a power law–tailed autocorrelation is assumed for the rainfall process. We further demonstrate how the downscaling method together with a Bartlett‐Lewis rainfall stochastic model may be used to generate hourly rainfall sequences that reproduce the observed small‐scale variability uniquely from daily statistics. Conclusions of a somewhat general nature are also drawn on the capability of finite memory stochastic models to reproduce the observed rainfall variability at different aggregation scales.

Bayesian merging of multiple climate model forecasts for seasonal hydrological predictions

This study uses a Bayesian approach to merge ensemble seasonal climate forecasts generated by multiple climate models for better probabilistic and deterministic forecasting. Within the Bayesian framework, the climatological distribution of the variable of interest serves as the prior, and the likelihood function is developed with a weighted linear regression between the climate model hindcasts and the corresponding observations. The resulting posterior distribution is the merged forecast, which represents our best estimate of the variable, including its mean and variance, given the current model forecast and knowledge about the model’s performance. The handling of multimodel climate forecasts and nonnormal distributed variables, such as precipitation, are two important extensions toward the application of the Bayesian merging approach for seasonal hydrological predictions. Two examples are presented as follows: seasonal forecast of sea surface temperature over equatorial Pacific and precipitation forecast over the Ohio River basin. Cross validation of these forecasts shows smaller root mean square error and smaller ranked probability score for the merged forecast as compared with raw forecasts from climate models and the climatological forecast, indicating an improvement in both deterministic and probabilistic forecast skills. Therefore there is great potential to apply this method to seasonal hydrological forecasting.

Putting the "vap" into evaporation

Abstract. In the spirit of the Special Issue of HESS to which it contributes, this paper documents the origin and development of the science of natural evaporation from land surfaces over the last 30–35 years, since the symposium A View from the Watershed was held to commemorate the opening of the new Institute of Hydrology (IH) building in 1973. Important subsequent technical progress includes the ability to measure routinely the diurnal cycle of near-surface meteorological variables using automatic weather stations, and of surface energy and momentum exchanges using automated implementations of the Bowen Ratio/Energy Budget technique and the Eddy Correlation technique, along with the capability to estimate the "fetch" for which these measurements apply. These improvements have been complemented by new methods to measure the separate components of evaporation, including: the interception process using randomly relocated below-canopy gauges, transpiration fluxes from individual leaves/shoots using porometers and from plants/plant components using stem-flow gauges and soil evaporation using micro-lysimeters and soil moisture depletion methods. In recent years progress has been made in making theory-based area-average estimates of evaporation using scintillometers, and model-based area-average estimates by assembling many streams of relevant data into Land Data Assimilation Systems. Theoretical progress has been made in extending near-surface turbulence theory to accommodate the effect of the "excess" boundary layer resistance to leaf-to-air transfer of energy and mass fluxes relative to that for momentum, and to allow for observed shortcoming in stability factors in the transition layer immediately above vegetation. Controversy regarding the relative merits of multi-layer model and "big leaf" representations of whole-canopy exchanges has been resolved in favour of the latter approach. Important gaps in the theory of canopy-atmosphere interactions have been filled, including recognising the need, separately, to represent dry-canopy and wet-canopy evaporation in models and the capability to describe wet-to-dry canopy transitions as well as the ability to describe sparse vegetation canopies which only partly cover the underlying soil. There is progress in methods of estimating crop water requirements, but an important recommendation of this paper is that this progress should continue by introducing use of an effective stomatal resistance rather than crop factors. The paper draws attention to relevant theoretical insight on this issue. Progress in theoretical understanding of evaporation processes has been used in the creation of numerous Land Surface Parameterisations (LSPs), the models used to represent land-surface interaction in climate and weather forecast models, and there have been important advances in describing the behaviour of plant stomata in LSPs. A major investment over the last 25 years in conducting Large-Scale Field Experiments, the better to measure, understand and model coupled land-surface/atmosphere interactions, has resulted in improvements in the capabilities of global climate models and the ability of mesoscale meteorological models to describe the enhanced circulation resulting from different forms of land-surface heterogeneity. Progress in understanding why early equations for potential evapotranspiration can be adequate in certain conditions is reviewed. The paper concludes with recommendations for future research.

Making climate model forecasts more useful

There is considerable potential for seasonal to inter-annual climate forecasts derived from dynamic models of the earth’s climate to be used widely to help improve management of important real-world issues in a variety of different areas (e.g. disaster management, agriculture, water management, health, natural resource management, food security, and insurance). Unfortunately, several factors currently inhibit this potential, e.g. low skill, low awareness, mismatches in what model forecasts can provide and what users need, and the complexity and probabilistic nature of the information provided. Substantial effort around the world is currently directed towards reducing these impediments. For example, climate model development continues behind the scenes, and techniques such as multi-model ensemble forecasting are progressing rapidly. Communication strategies that enable probabilistic information to be communicated more effectively have been developed and exciting developments such as the emergence of the Argo float program have dramatically improved our ability to initialise forecast systems. We can also look forward to greater computing power in the future, which will allow us to increase the resolution of the models used to perform forecasts. Research on the integration of climate forecasts with risk-management tools more useful to managers is also occurring. The great potential for much wider use of climate model forecasting cannot be denied. However, it will only be realised if models continue to be developed further, if climatic variability continues to be closely monitored from the surface, the atmosphere, the ocean, and from space, and if these data are made readily available to the research community.

Evaluation of the FSU synthetic superensemble performance for seasonal forecasts over the Euro-Mediterranean region

ABSTRACTThis paper deals with seasonal climate forecasting using as many as 13 coupled ocean—atmosphere models. An analysis of the individual model, multimodel ensemble, and FSU synthetic superensemble (FSUSSE) climate forecasts was performed for monthly and seasonal forecasts of precipitation, sea surface temperature (SST) and surface air temperature over the Euro-Mediterranean region including the land areas of Europe, North Africa and the Near East, during the period 1989–2001. In the FSUSSE methodology, forecasts are obtained by a weighted combination of the individual coupled ocean—atmosphere model forecasts based on a training period. The set of 13 individual climate forecast models utilized in this research is comprised of the seven models in the European suite of DEMETER models, a suite of four Florida State University models, the Australian POAMA model and the NCAR CCM3 model.The FSUSSE forecasts of seasonal precipitation anomalies were found to have the lowest root mean square (RMS) errors in comparison to the models in the multimodel ensemble, and their ensemble mean. However, the anomaly correlation (AC) coefficient results for seasonal precipitation anomaly forecasts by the FSUSSE were less impressive. The equitable threat scores for the FSUSSE forecasts of seasonal precipitation were found to be better than the various models in the multimodel ensemble, but those scores for the forecasts of positive seasonal anomalies were found to be worse than most of the models in the multimodel ensemble.The FSUSSE seasonal forecasts for SST and surface air temperature for the season considered (winter) were found to be excellent for both AC coefficients and RMS errors in the forecast anomalies.

Evaluation of the FSU synthetic superensemble performance for seasonal forecasts over the Euro-Mediterranean region

This paper deals with seasonal climate forecasting using as many as 13 coupled ocean–atmosphere models. An analysis of the individual model, multimodel ensemble, and FSU synthetic superensemble (FSUSSE) climate forecasts was performed for monthly and seasonal forecasts of precipitation, sea surface temperature (SST) and surface air temperature over the Euro-Mediterranean region including the land areas of Europe, North Africa and the Near East, during the period 1989–2001. In the FSUSSE methodology, forecasts are obtained by a weighted combination of the individual coupled ocean–atmosphere model forecasts based on a training period. The set of 13 individual climate forecast models utilized in this research is comprised of the seven models in the European suite of DEMETER models, a suite of four Florida State University models, the Australian POAMA model and the NCAR CCM3 model. The FSUSSE forecasts of seasonal precipitation anomalies were found to have the lowest root mean square (RMS) errors in comparison to the models in the multimodel ensemble, and their ensemble mean. However, the anomaly correlation (AC) coefficient results for seasonal precipitation anomaly forecasts by the FSUSSE were less impressive. The equitable threat scores for the FSUSSE forecasts of seasonal precipitation were found to be better than the various models in the multimodel ensemble, but those scores for the forecasts of positive seasonal anomalies were found to be worse than most of the models in the multimodel ensemble. The FSUSSE seasonal forecasts for SST and surface air temperature for the season considered (winter) were found to be excellent for both AC coefficients and RMS errors in the forecast anomalies.

A Multimodel Analysis, Validation, and Transferability Study of Global Soil Wetness Products

Abstract Multimodel ensemble forecasting has been shown to offer a systematic improvement in the skill of climate prediction with atmosphere and ocean circulation models. However, little such work has been done for the land surface component, an important lower boundary for weather and climate forecast models. In this study, the authors examine and evaluate several methods of combining individual global soil wetness products from uncoupled land surface model calculations and coupled land–atmosphere model reanalyses to produce an ensemble analysis. Analyses are verified against observations from the Global Soil Moisture Data Bank (GSMDB) with skill measured by correlation coefficient and root-mean-square error (RMSE). A preliminary transferability study is conducted as well for investigating the feasibility of transferring ensemble regression parameters within two specific regions (Illinois and east-central China) and between these two regions of similar climate and land use. The results show that when sufficient validation data are available, one can use a seasonally dependent linear regression to improve the skill of any individual model simulation of soil wetness. Further improvements in skill can be achieved with more sophisticated ensembling methods, such as the regression-adjusted multimodel ensemble mean analysis and regression-adjusted multimodel analysis. However, all the ensembling schemes involving regression usually do not help improve the skill scores as far as the simulation of anomalies of soil wetness is concerned. In the absence of calibration data, the simple arithmetic ensemble mean across multiple soil wetness products generally does as well or better than the best individual model at any location in the representation of both soil wetness and its anomaly. Transferability from one subset of stations from the Illinois or east-central China dataset to another gives satisfactory results. However, results are poor when transferring regression weights between different regions, even with similar climate regimes and land cover. Such an exercise helps us to understand better the virtues and limitations of various ensembling techniques and enables progress toward creating an optimum, model-independent analysis from a practical point of view.

Spatial and temporal variability in Moderate Resolution Imaging Spectroradiometer–derived surface albedo over global arid regions

We derive spectral and broadband surface albedo for global arid regions from data collected by the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra spacecraft, at 1 km spatial resolution for 2001. MODIS data show considerable spatial variability both across various arid regions of the globe (from the bright deserts of northern Africa and the Arabian peninsula to substantially less reflective American and Asian deserts) and within regions (variability related to soil and rock types). For example, over arid northern Africa and the Arabian peninsula, albedo in the visible broadband varies by a factor of over 2, from the brightest sand sheets to the darkest luvisols. Few, if any, global and regional land‐atmosphere models capture this observed spatial variability in surface albedo over arid regions. We suggest a scheme that relates soil groups (based on the United Nations Food and Agriculture Organization (FAO) soil classification) to MODIS‐derived surface albedo statistics. This approach allows for an efficient representation in climate and weather forecasting models of the observed spatial and temporal variability in surface albedo over global deserts. Observed variability in albedo was reduced to a small (1–13, depending on the region) number of soil‐related classes (end‐members) that could be used in climate models. We also addressed the temporal evolution of albedo during 2001 over global deserts. Regions/soils of stable albedo with very low temporal variability were identified. For other regions/soils, temporal signals in albedo were related to ephemeral inundation with water or variations in sample size.

Physically based parameterization of cirrus cloud formation for use in global atmospheric models

Motivated by the need to study the climatic impact of aerosol‐related cirrus cloud changes, a physically based parameterization scheme of ice initiation and initial growth of ice crystals in young cirrus clouds has been developed. The scheme tracks the number density and size of nucleated ice crystals as a function of vertical wind speed, temperature, ice saturation ratio, aerosol number size distributions, and preexisting cloud ice, allowing for competition between heterogeneous ice nuclei and liquid aerosol particles during freezing. Predictions of the parameterization are compared with numerical parcel simulations of ice nucleation and growth from atmospheric aerosols, with a special focus on explaining the indirect effects of ice nuclei on the properties of young cirrus clouds. The uncertainties of the parameterization are discussed and its implementation in a general circulation model is briefly outlined. This new scheme establishes a flexible framework for a comprehensive assessment of indirect aerosol effects on and properties of cirrus clouds in global climate, chemistry transport, and weather forecast models.

Integrating seasonal climate prediction and agricultural models for insights into agricultural practice

Interest in integrating crop simulation models with dynamic seasonal climate forecast models is expanding in response to a perceived opportunity to add value to seasonal climate forecasts for agriculture. Integrated modelling may help to address some obstacles to effective agricultural use of climate information. First, modelling can address the mismatch between farmers' needs and available operational forecasts. Probabilistic crop yield forecasts are directly relevant to farmers' livelihood decisions and, at a different scale, to early warning and market applications. Second, credible ex ante evidence of livelihood benefits, using integrated climate-crop-economic modelling in a value-of-information framework, may assist in the challenge of obtaining institutional, financial and political support; and inform targeting for greatest benefit. Third, integrated modelling can reduce the risk and learning time associated with adaptation and adoption, and related uncertainty on the part of advisors and advocates. It can provide insights to advisors, and enhance site-specific interpretation of recommendations when driven by spatial data. Model-based 'discussion support systems' contribute to learning and farmer-researcher dialogue. Integrated climate-crop modelling may play a genuine, but limited role in efforts to support climate risk management in agriculture, but only if they are used appropriately, with understanding of their capabilities and limitations, and with cautious evaluation of model predictions and of the insights that arises from model-based decision analysis.

A retrospective assessment of National Centers for Environmental Prediction climate model–based ensemble hydrologic forecasting in the western United States

We assess the potential forecast skill of a climate model–based approach for seasonal ensemble hydrologic and streamflow forecasting for the western United States. By using climate model ensemble forecasts and ensembles formed via the resampling of observations, we distinguish hydrologic forecast skill resulting from the predictable evolution of initial hydrologic conditions from that derived from the climate model forecasts. Monthly climate model ensembles of precipitation and temperature produced by the National Centers for Environmental prediction global spectral model (GSM) are downscaled for use as forcings of the variable infiltration capacity (VIC) hydrologic model. VIC then simulates ensembles of streamflow and spatially distributed hydrologic variables such as snowpack, soil moisture, and runoff. The regional averages of the ensemble forcings and derived hydrologic variables were evaluated over five regions: the Pacific Northwest, California, the Great Basin, the Colorado River basin, and the upper Rio Grande River basin. The skill assessment focuses on a retrospective 21‐year period (1979–1999) during which GSM retrospective forecast ensembles (termed hindcasts), created using similar procedures to GSM real‐time forecasts, are available. The observational verification data set for the hindcasts was a retrospective hydroclimatology at 1/8°–1/4° consisting of gridded observations of temperature and precipitation and gridded hydrologic simulation results (for hydrologic variables and streamflow) based on the observed meteorological inputs. The GSM hindcast skill was assessed relative to that of a naive ensemble climatology forecast and to that of ensemble streamflow prediction (ESP) hindcasts, a forecast baseline sharing the same initial condition information as the GSM‐based hindcasts. We found that the unconditional (all years) GSM hindcasts for regionally averaged variables provided practically no skill improvement over the ESP hindcasts and did not lead to improved regional hydrologic variable or streamflow forecasts. GSM‐based conditional (strong El Niño–Southern Oscillation (ENSO) years only) hindcasts, however, had higher skill in a number of hindcast months for surface air temperature, with mixed results (better and worse) for precipitation, depending on location and season. Consequently, for California and to a lesser extent the Pacific Northwest and Great Basin, hydrologic hindcast skill in winter and fall increased enough under the strong ENSO composite that streamflow hindcasts were measurably better than with ESP. The opposite was found, however, for the Colorado and upper Rio Grande River basins, where the ENSO teleconnection is somewhat weaker.

Comparison, Validation, and Transferability of Eight Multiyear Global Soil Wetness Products

Abstract The characteristics of eight global soil wetness products, three produced by land surface model calculations, three from coupled land–atmosphere model reanalyses, and two from microwave remote sensing estimates, have been examined. The goal of this study is to determine whether there exists an optimal dataset for the initialization of the land surface component of global weather and climate forecast models. Their abilities to simulate the phasing of the annual cycle and to accurately represent interannual variability in soil wetness by comparing to available in situ measurements are validated. Because soil wetness climatologies vary greatly among land surface models, and models have different operating ranges for soil wetness (i.e., very different mean values, variances, and hydrologically critical thresholds such as the point where evaporation occurs at the potential rate or where surface runoff begins), one cannot simply take the soil wetness field from one product and apply it to an arbitrary land surface scheme (LSS) as an initial condition without experiencing some sort of initialization shock. A means of renormalizing soil wetness is proposed based on the local statistical properties of this field in the source and target models, to allow a large number of climate models to apply the same initialization in multimodel studies or intercomparisons. As a test of feasibility, renormalization among the model-derived products is applied to see how it alters the character of the soil wetness climatologies.

THE BALTEX BRIDGE CAMPAIGN: An Integrated Approach for a Better Understanding of Clouds

Clouds affect our daily life in many ways. They dominate our perception of weather and, thus, have an enormous influence on our everyday activities and our health. This fact is completely at odds with our knowledge about clouds, their representation in climate and weather forecast models, and our ability to predict clouds. It is their high variability in time and space that makes clouds both hard to monitor and to model. Clouds are the major concern in the climate modeling community, as stated by the Intergovernmental Panel on Climate Change (IPCC; information available online at www.ipcc.ch) “the most urgent scientific problems requiring attention to determine the rate and magnitude of climate change and sea level rise are the factors controlling the distribution of clouds and their radiative characteristics.” A similar conclusion was obtained within the Atmospheric Model Intercomparison Project (AMIP; e.g., Gates et al. 1999). The great challenge of climate research is to correctly account for the fact that the global state of our climate system is largely driven by various small-scale processes and their interaction with each other. Clouds are the most visible examples of this situation. On a global scale, clouds have a strong cooling effect on our climate: more solar radiation is reflected back to space than thermal surface radiation is trapped in the atmosphere. However, because radiation reacts on the instantaneous cloudy atmosphere and not on some climatological mean, the physical processes leading to the overall radiative effect strongly depend on the spatial distribution and structure of clouds.

Long‐range experimental hydrologic forecasting for the eastern United States

We explore a strategy for long‐range hydrologic forecasting that uses ensemble climate model forecasts as input to a macroscale hydrologic model to produce runoff and streamflow forecasts at spatial and temporal scales appropriate for water management. Monthly ensemble climate model forecasts produced by the National Centers for Environmental Prediction/Climate Prediction Center global spectral model (GSM) are bias corrected, downscaled to 1/8° horizontal resolution, and disaggregated to a daily time step for input to the Variable Infiltration Capacity hydrologic model. Bias correction is effected by evaluating the GSM ensemble forecast variables as percentiles relative to the GSM model climatology and then extracting the percentiles' associated variable values instead from the observed climatology. The monthly meteorological forecasts are then interpolated to the finer hydrologic model scale, at which a daily signal that preserves the forecast anomaly is imposed through resampling of the historic record. With the resulting monthly runoff and streamflow forecasts for the East Coast and Ohio River basin, we evaluate the bias correction and resampling approaches during the southeastern United States drought from May to August 2000 and also for the El Niño conditions of December 1997 to February 1998. For the summer 2000 study period, persistence in anomalous initial hydrologic states predominates in determining the hydrologic forecasts. In contrast, the El Niño‐condition hydrologic forecasts derive direction both from the climate model forecast signal and the antecedent land surface state. From a qualitative standpoint the hydrologic forecasting strategy appears successful in translating climate forecast signals to hydrologic variables of interest for water management.

Ice crystal number concentration versus temperature for climate studies

AbstractIce crystal number concentration (Ni) is an important parameter, having a strong influence on the calculation of cloud optical and microphysical parameters. Cloud and precipitation parameterizations within climate and weather forecasting models, affecting the heat and moisture budget of the atmosphere, cannot be determined accurately if Ni is not estimated correctly. Previous studies of ice crystal number concentration versus temperature (T) have shown that Ni–T relationships are not unique. The present study uses observations made in the glaciated regions of stratiform clouds from two Arctic and two mid‐latitude field projects to study the Ni versus temperature relationship. Scatter plots of Ni versus T at the ice particle measurement level do not show a good correlation with T for ice crystals at sizes less than 1000 µm. For a given temperature, the variation in Ni is found to be up to two to three orders of magnitude for ice crystals with sizes larger than approximately 100 µm. A significant Ni–T relationship is found for precipitation sized particles with sizes greater than 1000 µm. The ice particle concentration for sizes between 100 and 1000 µm varied from 0.1 to 100 L−1, independent of geographic location where the measurements were made. Based on this work, it is concluded that modelling studies should be tested for the possible variations in Ni versus T. Copyright © 2001 Royal Meteorological Society

Symmetric Stress Tensor Formulation of Horizontal Momentum Diffusion in Global Models of Atmospheric Circulation

Abstract In climate and weather forecast models, small-scale turbulence in the free atmosphere is usually parameterized by horizontal diffusion of horizontal momentum. This study proposes a formulation that is based on a symmetric stress tensor. The advantage over conventional methods is twofold. First, the Eulerian law of angular momentum conservation is fulfilled. Second, a self-consistent formulation of the momentum and thermodynamic equations of motion becomes possible due to incorporation of the local frictional heating rate, that is, the proper dissipation. The importance of these issues is demonstrated by numerical experiments performed with a simple general circulation model. For example, the new scheme precisely accounts for the irreversible increase of total potential energy during the decay of a baroclinic life cycle. Also the stress generated by horizontal momentum diffusion is found to be significant in the angular momentum budget of multiple life cycle experiments.