Abstract

Using a subgrid distribution for the soil moisture content derived from a macroscale hydrologic model, it is investigated how lateral subgrid variations in the soil moisture content impact both the daily and the seasonal cycle of the spatially averaged surface flux densities and near-surface meteorology. In agreement with earlier studies it is found that in wet conditions the use of one uniform volumetric soil moisture content, referred to as the bulk approach, gives larger estimates of the latent heat flux density than a quasi-distributed approach where the lateral variation in the volumetric soil moisture content is taken into account. In dry conditions the bulk approach gives lower estimates of the latent heat flux density than the quasi-distributed approach. In this study, the differences between the flux density estimates obtained by both approaches appear even when the developing convective boundary layer is allowed to feed back on the surface. It is also shown that differences in the estimated surface flux densities lead to differences between the predicted atmospheric specific humidity and the predicted near-surface temperature. The differences due to the subgrid variations in the soil moisture content appear to impact the seasonal hydrologic balance. Especially for dry climates, the quasi-distributed approach predicts a more gradual decrease of the evapotranspiration during the dry season, resulting in a larger cumulative evapotranspiration over the dry season. Thus, taking account of the spatial heterogeneity of the soil moisture content is a prerequisite for a proper representation of the seasonal hydrological cycle within large-scale atmospheric models.

Abstract

Two simplifying assumptions adopted in the current ECMWF surface scheme are explored: a uniform skin temperature for all grid-box fractions with variable latent heat release and a fixed value of an effective heat conductivity defining the soil heat flux density. This paper proposes relatively simple modifications of the ECMWF scheme with a better physical basis, without large input or computer infrastructure requirements.

A uniform skin temperature overestimates evaporation from relatively wet surface fractions when the other surface components are dry and warm. This is shown to be the case for an evaporating soil after rain and vegetation evaporation in a sparse Mediterranean vineyard canopy. Allowing different temperatures for each surface fraction significantly reduces the overestimations and introduces only little additional computation.

The default effective conductivity value (7 W m⁻²K⁻¹) employed by the current ECMWF scheme is shown to be too low for the sparse vineyard canopy. By raising the conductivity to 17 W m⁻² K⁻¹ for the bare-soil part of the surface, the daytime simulated soil heat flux was improved considerably.

Abstract

This paper deals with the parameter kB⁻¹, the logarithm of the ratio between momentum and heat roughness length, of sparsely vegetated surfaces and bare soil. The bare soil surface is included as a reference, since it is fairly homogenous and smooth, having no distinguishable roughness elements. The mean value of kB⁻¹ is about 8 for the vineyard and 12 for the savannah. These values are significantly greater than kB⁻¹ = 2, which is usually assumed to hold for vegetation. The mean value of kB⁻¹ for bare soil is small and negative, which agrees with the literature. A large variation of kB⁻¹ during the day is measured for all three surfaces. This behavior has been observed for sparse vegetation in previous studies. Some authors explained the phenomenon with a vertical movement of the source of heat through the day as solar angle varies, or with the use of an inappropriate value of effective surface temperature to calculate kB⁻¹. For the first time, this diurnal variation is measured for a smooth surface, the bare soil, for which neither explanation is valid. A sensitivity study reveals that the calculated kB⁻¹ is very sensitive to measuring errors in the micrometeorological variables and errors in the roughness length for momentum. This explains the large range in observed kB⁻¹ values for one particular surface type. In addition, several semiempirical expressions for kB⁻¹ from the literature are tested. Two well-established formulas, both based on a simple combination of Reynolds and Prandtl numbers, appear to produce the best estimates of daily averaged kB⁻¹ values. None of the formulas are able to describe the diurnal variation. The authors conclude that the concept of kB⁻¹ is questionable as it is based upon extrapolating a theoretical profile through a region where this profile does not hold, toward a “surface temperature” that is difficult to define and to measure. It should therefore be avoided in meteorological models, for example, by applying canopy boundary layer resistances. Unfortunately, in remote sensing, the bulk transfer equations are up to now the only option, which therefore requires the use of kB⁻¹.

Abstract

Observations have shown that differences in surface energy fluxes over grasslands and forests are amplified during heat waves. The role of land–atmosphere feedbacks in this process is still uncertain. In this study, a single-column model (SCM) is used to investigate the difference between forest and grassland in their energy response to heat waves. Three simulations for the period 2005–11 were carried out: a control run using vegetation characteristics for Cabauw (the Netherlands), a run where the vegetation is changed to 100% forest, and a run with 100% short grass as vegetation. A surface evaporation tendency equation is used to analyze the impact of the land–atmosphere feedbacks on evapotranspiration and sensible heat release under normal summer and heat wave conditions with excessive shortwave radiation.

Land–atmosphere feedbacks modify the contrast in surface energy fluxes between forest and grass, particularly during heat wave conditions. The surface resistance feedback has the largest positive impact, while boundary layer feedbacks generally tend to reduce the contrast. Overall, forests give higher air temperatures and drier atmospheres during heat waves. In offline land surface model simulations, the difference between forest and grassland during heat waves cannot be diagnosed adequately owing to the absence of boundary layer feedbacks.

Abstract

This study investigates likely changes in mean and extreme precipitation over southern Africa in response to changes in radiative forcing using an ensemble of global climate models prepared for the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4). Extreme seasonal precipitation is defined in terms of 10-yr return levels obtained by inverting a generalized Pareto distribution fitted to excesses above a predefined high threshold. Both present (control) and future climate precipitation extremes are estimated. The future-to-control climate ratio of 10-yr return levels is then used as an indicator for the likely changes in extreme seasonal precipitation.

A Bayesian approach to multimodel ensembling is adopted. The relative weights assigned to each of the model simulations is determined from bias, convergence, and correlation. Using this method, the probable limits of the changes in mean and extreme precipitation are estimated from their posterior distribution.

Over the western parts of southern Africa, an increase in the severity of dry extremes parallels a statistically significant decrease in mean precipitation during austral summer months. A notable delay in the onset of the rainy season is found in almost the entire region. An early cessation is found in many parts. This implies a statistically significant shortening of the rainy season.

A substantial reduction in moisture influx from the southwestern Indian Ocean during austral spring is projected. This and the preaustral spring moisture deficits are possible mechanisms delaying the rainfall onset in southern Africa. A possible offshore (northeasterly) shift of the tropical–temperate cloud band is consistent with more severe droughts in the southwest of southern Africa and enhanced precipitation farther north in Zambia, Malawi, and northern Mozambique.

This study shows that changes in the mean vary on relatively small spatial scales in southern Africa and differ between seasons. Changes in extremes often, but not always, parallel changes in the mean precipitation.

Abstract

The objective of the present study was to assess the performance and recent improvements of the land surface scheme used operationally in the European Centre for Medium-Range Weather Forecasts (ECMWF) in a Scandinavian boreal forest climate/ecosystem. The previous (the 1999 scheme of P. Viterbo and A. K. Betts) and the new (Tiled ECMWF Surface Scheme for Exchange Processes over Land, TESSEL) surface schemes were validated by single-column runs against data from NOPEX (Northern Hemisphere Climate-Processes Land-Surface Experiment). Driving and validation datasets were prepared for a 3-yr period (1994–96). The new surface scheme, with separate surface energy balances for subgrid fractions (tiling), improved predictions of seasonal as well as diurnal variation in surface energy fluxes in comparison with the old scheme. Simulated wintertime evaporation improved significantly as a consequence of the introduced additional aerodynamic resistance for evaporation from snow lying under high vegetation. Simulated springtime evaporation also improved because the limitation of transpiration in frozen soils was now accounted for. However, downward sensible heat flux was still underestimated during winter, especially at nighttime, whereas soil temperatures were underestimated in winter and overestimated in summer. The new scheme also underestimated evaporation during dry periods in summer, whereas soil moisture was overestimated. Sensitivity tests showed that further improvements of simulated surface heat fluxes and soil temperatures could be obtained by calibration of parameters governing the coupling between the surface and the atmosphere and the ground heat flux, and parameters governing the water uptake by the vegetation. Model performance also improved when the seasonal variation in vegetation properties was included.

Abstract

Skillful forecasts of the Indian summer monsoon rainfall (ISMR) at long lead times (4–5 months in advance) pose great challenges due to strong internal variability of the monsoon system and nonstationarity of climatic drivers. Here, we use an advanced causal discovery algorithm coupled with a response-guided detection step to detect low-frequency, remote processes that provide sources of predictability for the ISMR. The algorithm identifies causal precursors without any a priori assumptions, apart from the selected variables and lead times. Using these causal precursors, a statistical hindcast model is formulated to predict seasonal ISMR that yields valuable skill with correlation coefficient (CC) ~0.8 at a 4-month lead time. The causal precursors identified are generally in agreement with statistical predictors conventionally used by the India Meteorological Department (IMD); however, our methodology provides precursors that are automatically updated, providing emerging new patterns. Analyzing ENSO-positive and ENSO-negative years separately helps to identify the different mechanisms at play during different years and may help to understand the strong nonstationarity of ISMR precursors over time. We construct operational forecasts for both shorter (2-month) and longer (4-month) lead times and show significant skill over the 1981–2004 period (CC ~0.4) for both lead times, comparable with that of IMD predictions (CC ~0.3). Our method is objective and automatized and can be trained for specific regions and time scales that are of interest to stakeholders, providing the potential to improve seasonal ISMR forecasts.

Abstract

The Protocol for the Analysis of Land Surface Models (PALS) Land Surface Model Benchmarking Evaluation Project (PLUMBER) was designed to be a land surface model (LSM) benchmarking intercomparison. Unlike the traditional methods of LSM evaluation or comparison, benchmarking uses a fundamentally different approach in that it sets expectations of performance in a range of metrics a priori—before model simulations are performed. This can lead to very different conclusions about LSM performance. For this study, both simple physically based models and empirical relationships were used as the benchmarks. Simulations were performed with 13 LSMs using atmospheric forcing for 20 sites, and then model performance relative to these benchmarks was examined. Results show that even for commonly used statistical metrics, the LSMs’ performance varies considerably when compared to the different benchmarks. All models outperform the simple physically based benchmarks, but for sensible heat flux the LSMs are themselves outperformed by an out-of-sample linear regression against downward shortwave radiation. While moisture information is clearly central to latent heat flux prediction, the LSMs are still outperformed by a three-variable nonlinear regression that uses instantaneous atmospheric humidity and temperature in addition to downward shortwave radiation. These results highlight the limitations of the prevailing paradigm of LSM evaluation that simply compares an LSM to observations and to other LSMs without a mechanism to objectively quantify the expectations of performance. The authors conclude that their results challenge the conceptual view of energy partitioning at the land surface.

Abstract

The project Land-Use and Climate, Identification of Robust Impacts (LUCID) was conceived to address the robustness of biogeophysical impacts of historical land use–land cover change (LULCC). LUCID used seven atmosphere–land models with a common experimental design to explore those impacts of LULCC that are robust and consistent across the climate models. The biogeophysical impacts of LULCC were also compared to the impact of elevated greenhouse gases and resulting changes in sea surface temperatures and sea ice extent (CO2SST). Focusing the analysis on Eurasia and North America, this study shows that for a number of variables LULCC has an impact of similar magnitude but of an opposite sign, to increased greenhouse gases and warmer oceans. However, the variability among the individual models’ response to LULCC is larger than that found from the increase in CO2SST. The results of the study show that although the dispersion among the models’ response to LULCC is large, there are a number of robust common features shared by all models: the amount of available energy used for turbulent fluxes is consistent between the models and the changes in response to LULCC depend almost linearly on the amount of trees removed. However, less encouraging is the conclusion that there is no consistency among the various models regarding how LULCC affects the partitioning of available energy between latent and sensible heat fluxes at a specific time. The results therefore highlight the urgent need to evaluate land surface models more thoroughly, particularly how they respond to a perturbation in addition to how they simulate an observed average state.

Abstract

The second phase of the Global Land–Atmosphere Coupling Experiment (GLACE-2) is a multi-institutional numerical modeling experiment focused on quantifying, for boreal summer, the subseasonal (out to two months) forecast skill for precipitation and air temperature that can be derived from the realistic initialization of land surface states, notably soil moisture. An overview of the experiment and model behavior at the global scale is described here, along with a determination and characterization of multimodel “consensus” skill. The models show modest but significant skill in predicting air temperatures, especially where the rain gauge network is dense. Given that precipitation is the chief driver of soil moisture, and thereby assuming that rain gauge density is a reasonable proxy for the adequacy of the observational network contributing to soil moisture initialization, this result indeed highlights the potential contribution of enhanced observations to prediction. Land-derived precipitation forecast skill is much weaker than that for air temperature. The skill for predicting air temperature, and to some extent precipitation, increases with the magnitude of the initial soil moisture anomaly. GLACE-2 results are examined further to provide insight into the asymmetric impacts of wet and dry soil moisture initialization on skill.