Rapid vegetation responses and feedbacks amplify climate model response to snow cover changes

We investigate the response of a climate system model to two different methods for estimating snow cover fraction. In the control case, snow cover fraction changes gradually with snow depth; in the alternative scenarios (one with prescribed vegetation and one with dynamic vegetation), snow cover fraction initially increases with snow depth almost twice as fast as the control method. In cases where the vegetation was fixed (prescribed), the choice of snow cover parameterization resulted in a limited model response. Increased albedo associated with the high snow caused some moderate localized cooling (3–5°C), mostly at very high latitudes (>70°N) and during the spring season. During the other seasons, however, the cooling was not very extensive. With dynamic vegetation the change is much more dramatic. The initial increases in snow cover fraction with the new parameterization lead to a large-scale southward retreat of boreal vegetation, widespread cooling, and persistent snow cover over much of the boreal region during the boreal summer. Large cold anomalies of up to 15°C cover much of northern Eurasia and North America and the cooling is geographically extensive in the northern hemisphere extratropics, especially during the spring and summer seasons. This study demonstrates the potential for dynamic vegetation within climate models to be quite sensitive to modest forcing. This highlights the importance of dynamic vegetation, both as an amplifier of feedbacks in the climate system and as an essential consideration when implementing adjustments to existing model parameters and algorithms.     

Shear-Stress Partitioning in Live Plant Canopies and Modifications to Raupach’s Model

The spatial peak surface shear stress t_S^￠￠{\tau _S^{\prime\prime}} on the ground beneath vegetation canopies is responsible for the onset of particle entrainment and its precise and accurate prediction is essential when modelling soil, snow or sand erosion. This study investigates shear-stress partitioning, i.e. the fraction of the total fluid stress on the entire canopy that acts directly on the surface, for live vegetation canopies (plant species: Lolium perenne) using measurements in a controlled wind-tunnel environment. Rigid, non-porous wooden blocks instead of the plants were additionally tested for the purpose of comparison since previous wind-tunnel studies used exclusively artificial plant imitations for their experiments on shear-stress partitioning. The drag partitioning model presented by Raupach (Boundary-Layer Meteorol 60:375–395, 1992) and Raupach et al. (J Geophys Res 98:3023–3029, 1993), which allows the prediction of the total shear stress τ on the entire canopy as well as the peak (t_S ^￠￠/t)^1/2{(\tau _S ^{\prime\prime}/\tau )^{1/2}} and the average (t_S^￠/t)^1/2{(\tau _S^{\prime}/\tau )^{1/2}} shear-stress ratios, is tested against measurements to determine the model parameters and the model’s ability to account for shape differences of various roughness elements. It was found that the constant c, needed to determine the total stress τ and which was unspecified to date, can be assumed a value of about c = 0.27. Values for the model parameter m, which accounts for the difference between the spatial surface average t_S^￠{\tau _S^{\prime}} and the peak t_S ^￠￠{\tau _S ^{\prime\prime}} shear stress, are difficult to determine because m is a function of the roughness density, the wind velocity and the roughness element shape. A new definition for a parameter a is suggested as a substitute for m. This a parameter is found to be more closely universal and solely a function of the roughness element shape. It is able to predict the peak surface shear stress accurately. Finally, a method is presented to determine the new a parameter for different kinds of roughness elements.     

Burgundy regional climate change and its potential impact on grapevines

ARPEGE general circulation model simulations were dynamically downscaled by The Weather Research and Forecasting Model (WRF) for the study of climate change and its impact on grapevine growth in Burgundy region in France by the mid twenty-first century. Two time periods were selected: 1970–1979 and 2031–2040. The WRF model driven by ERA-INTERIM reanalysis data was validated against in situ surface temperature observations. The daily maximum and minimum surface temperature (T_max and T_min) were simulated by the WRF model at 8?×?8?km horizontal resolution. The averaged daily T_max for each month during 1970–1979 have good agreement with observations, the averaged daily T_min have a warm bias about 1–2?K. The daily T_max and T_min for each month (domain averaged) during 2031–2040 show a general increase. The largest increment (~3?K) was found in summer. The smallest increments (<1?K) were found in spring and fall. The spatial distribution of temperature increment shows a strong meridional gradient, high in south in summer, reversing in winter. The resulting potential warming rate in summer is equivalent to 4.7?K/century under the IPCC A2 emission scenario. The dynamically downscaled T_max and T_min were used to simulate the grape (Pinot noir grape variety) flowering and véraison dates. For 2031–2040, the projected dates are 8 and 12?days earlier than those during 1970–1979, respectively. The simulated hot days increase more than 50% in the two principal grapevine regions. They show strong impact on Pinot noir development.     

Weather regimes over Senegal during the summer monsoon season using self-organizing maps and hierarchical ascendant classification. Part II: interannual time scale

The aim of this work is to define over the period 1979–2002 the main synoptic weather regimes relevant for understanding the daily variability of rainfall during the summer monsoon season over Senegal. “Interannual” synoptic weather regimes are defined by removing the influence of the mean 1979–2002 seasonal cycle. This is different from Part I where the seasonal evolution of each year was removed, then removing also the contribution of interannual variability. As in Part I, the self-organizing maps approach, a clustering methodology based on non-linear artificial neural network, is combined with a hierarchical ascendant classification to compute these regimes. Nine weather regimes are identified using the mean sea level pressure and 850?hPa wind field as variables. The composite circulation patterns of all these nine weather regimes are very consistent with the associated anomaly patterns of precipitable water, mid-troposphere vertical velocity and rainfall. They are also consistent with the distribution of rainfall extremes. These regimes have been then gathered into different groups. A first group of four regimes is included in an inner circuit and is characterized by a modulation of the semi-permanent trough located along the western coast of West Africa and an opposite modulation on the east. This circuit is important because it associates the two wettest and highly persistent weather regimes over Senegal with the driest and the most persistent one. One derivation of this circuit is highlighted, including the two driest regimes and the most persistent one, what can provide important dry sequences occurrence. An exit of this circuit is characterised by a filling of the Saharan heat low. An entry into the main circuit includes a southward location of the Saharan heat low followed by its deepening. The last weather regime is isolated from the other ones and it has no significant impact on Senegal. It is present in June and September, and missing in July and August, meaning that this is a weather regime more specific of the intermediate seasons than the summer. It is included in a large-scale pattern covering the northern latitudes of Europe. The correspondence between these “interannual” synoptic weather regimes and the “pure” synoptic regimes defined in Part I has been established. By selecting a high statistical significance level for these correspondences, each of five out of nine “interannual” weather regimes has a close correspondence with one “pure” synoptic weather regime, one out of them have links with two “pure” regimes, and the last three regimes have no significant correspondence in terms of “pure” regimes. However when considering more moderate links, two out of these three regimes show a connection with a “pure” regime, and the last one remains isolated. The ensemble of the weather regimes occurrences can explain a significant part of interannual variability of summer rainfall amount over Senegal, especially linked to the driest and the wettest weather regimes occurrences. It is also shown that Senegal rainfall state is very sensitive to a small displacement or deformation of the weather regime patterns.     

Uncertainties in simulating regional climate of Southern Africa: sensitivity to physical parameterizations using WRF

This study aims at quantifying seasonal biases of regional climate model outputs during southern African summer, against a dense in situ measurement network (daily rain-gauge and surface air temperature records, and 12?h UTC radiosondes), and uncertainties associated with some physical parameterizations. Using the non-hydrostatic Advanced Research Weather Forecast (WRF) laterally forced by ERA40 reanalysis, twenty-seven experiments configured with three schemes of cumulus (CU), planetary boundary layer (PBL) and microphysics (MP), are performed at 35?km horizontal resolution during the core of a summer rainy season (December 1993 to February 1994 season) representative of the South African rainfall climatology. WRF simulates accurately seasonal large-scale rainfall patterns, as well as seasonal gradients of South African rainfall and 2-m temperature, and seasonal vertical profiles of the air temperature and humidity. However seasonal biases fluctuate strongly from an experiment to another, denoting considerable uncertainties generated by the physical package. Rainfall amounts are the most sensitive parameter to the tested schemes. Their geography, intensity, and intraseasonal characteristics are predominantly sensitive to CU schemes, and much less to PBL and MP schemes. Some CU-PBL combinations produce additive effects, which can dramatically either reduce or increase biases. Satisfactory configurations are found for South African climate, which would not have been possible without testing numerous physical parameterizations.