Bivariate colour maps for visualizing climate data

The increasing availability of gridded, high-resolution, multivariate climatological data sets calls for innovative approaches to visualize inter-variable relations. In this study, we present a methodology, based on properties of common colour schemes, to plot two variables in a single colour map by using a two-dimensional colour legend for both sequential and diverging data. This is especially suited for climate data as the spatial distribution of the relation between different variables is often as important as the distribution of variables individually. Two example applications are given to illustrate the use of the method: one that shows the global distribution of climate based on observed temperature and relative humidity, and the other showing the distribution of recent changes in observed temperature and precipitation over Europe. A flexible and easy-to-implement method is provided to construct different colour legends for sequential and diverging data

Damage Mechanisms in Tapered Composite Structures Under Static and Fatigue Loading

In this work an integrated computational/experimental approach was developed to validate the predictive capabilities of State-of-the-Art (SoA) Progressive Damage Analysis (PDA) methods and tools. Specifically, a tapered composite structure incorporating ply-drops typical in the aerospace industry to spatially vary structural thickness was tested under static tension and cyclic tension fatigue loads. The data acquired from these tests included quantitative metrics such as pre-peak stiffness, peak load, location of delamination damage onset, and growth of delaminations as functions of applied static and fatigue loads. It was shown that the PDA tools were able to predict the pre-peak stiffness and peak load within 10% of experimental average, thereby meeting and exceeding the pre-defined success criteria. Additionally, it was shown that the PDA tools were able to accurately predict the location of delamination onset and satisfactorily predict delamination growth under static tension loading. Overall, good correlations were achieved between modeling and experiments

Variability of soil moisture and sea surface temperatures similarly important for warm-season land climate in the community earth system model

Both sea surface temperatures (SSTs) and soil moisture (SM) can influence climate over land. This paper presents a comprehensive comparison of SM versus SST impacts on land climate in the warm season. The authors perform fully coupled ensemble experiments with the Community Earth System Model in which they prescribe SM or SSTs to the long-term median seasonal cycles. It is found that SM variability overall impacts warm-season land climate to a similar extent as SST variability, in the midlatitudes, tropics, and subtropics. Removing SM or SST variability impacts land climate means and reduces land climate variability at different time scales by 10%-50% (temperature) and 0%-10% (precipitation). Both SM- and SST-induced changes are strongest for hot temperatures (up to 50%) and for extreme precipitation (up to 20%). These results are qualitatively similar for the present day and the end of the twenty-first century. Removed SM variability affects surface climate through corresponding variations in surface energy fluxes, and this is controlled to first order by the land-atmosphere coupling strength and the natural SM variability. SST-related changes are partly controlled by the relation of local temperature or precipitation with the El Nino-Southern Oscillation. In addition, in specific regions SST-induced SM changes alter the "direct" SST-induced climate changes; on the other hand, SM variability is found to slightly affect SSTs in some regions. Nevertheless a large level of independence is found between SM-climate and SST-climate coupling. This highlights the fact that SM conditions can influence land climate variables independently of any SST effects and that (initial) soil moisture anomalies can provide valuable information in (sub)seasonal weather forecasts

Parameter Sensitivity in LSMs: An Analysis Using Stochastic Soil Moisture Models and ELDAS Soil Parameters

Integration of simulated and observed states through data assimilation as well as model evaluation requires a realistic representation of soil moisture in land surface models (LSMs). However, soil moisture in LSMs is sensitive to a range of uncertain input parameters, and intermodel differences in parameter values are often large. Here, the effect of soil parameters on soil moisture and evapotranspiration are investigated by using parameters from three different LSMs participating in the European Land Data Assimilation System (ELDAS) project. To prevent compensating effects from other than soil parameters, the effects are evaluated within a common framework of parsimonious stochastic soil moisture models. First, soil parameters are shown to affect soil moisture more strongly than the average evapotranspiration. In arid climates, the effect of soil parameters is on the variance rather than the mean, and the intermodel flux differences are smallest. Soil parameters from the ELDAS LSMs differ strongly, most notably in the available moisture content between the wilting point and the critical moisture content, which differ by a factor of 3. The ELDAS parameters can lead to differences in mean volumetric soil moisture as high as 0.10 and an average evapotranspiration of 10%–20% for the investigated parameter range. The parsimonious framework presented here can be used to investigate first-order parameter sensitivities under a range of climate conditions without using full LSM simulations. The results are consistent with many other studies using different LSMs under a more limited range of possible forcing condition

Inferring soil moisture memory from streamflow observations using a simple water balance model

Soil moisture is known for its integrative behavior and resulting memory characteristics. Soil moisture anomalies can persist for weeks or even months into the future, making initial soil moisture a potentially important contributor to skill in weather forecasting. A major difficulty when investigating soil moisture and its memory using observations is the sparse availability of long-term measurements and their limited spatial representativeness. In contrast, there is an abundance of long-term streamflow measurements for catchments of various sizes across the world. The authors investigate in this study whether such streamflow measurements can be used to infer and characterize soil moisture memory in respective catchments. Their approach uses a simple water balance model in which evapotranspiration and runoff ratios are expressed as simple functions of soil moisture; optimized functions for the model are determined using streamflow observations, and the optimized model in turn provides information on soil moisture memory on the catchment scale. The validity of the approach is demonstrated with data from three heavily monitored catchments. The approach is then applied to streamflow data in several small catchments across Switzerland to obtain a spatially distributed description of soil moisture memory and to show how memory varies, for example, with altitude and topography

Propagation of soil moisture memory to streamflow and evapotranspiration in Europe

As a key variable of the land-climate system soil moisture is a main driver of streamflow and evapotranspiration under certain conditions. Soil moisture furthermore exhibits outstanding memory (persistence) characteristics. Many studies also report distinct low frequency variations for streamflow, which are likely related to soil moisture memory. Using data from over 100 near-natural catchments located across Europe, we investigate in this study the connection between soil moisture memory and the respective memory of streamflow and evapotranspiration on different time scales. For this purpose we use a simple water balance model in which dependencies of runoff (normalised by precipitation) and evapotranspiration (normalised by radiation) on soil moisture are fitted using streamflow observations. The model therefore allows us to compute the memory characteristics of soil moisture, streamflow and evapotranspiration on the catchment scale. We find considerable memory in soil moisture and streamflow in many parts of the continent, and evapotranspiration also displays some memory at monthly time scale in some catchments. We show that the memory of streamflow and evapotranspiration jointly depend on soil moisture memory and on the strength of the coupling of streamflow and evapotranspiration to soil moisture. Furthermore, we find that the coupling strengths of streamflow and evapotranspiration to soil moisture depend on the shape of the fitted dependencies and on the variance of the meteorological forcing. To better interpret the magnitude of the respective memories across Europe, we finally provide a new perspective on hydrological memory by relating it to the mean duration required to recover from anomalies exceeding a certain threshold

Role of soil moisture versus recent climate change for the 2010 heat wave in western Russia

The severe 2010 heat wave in western Russia was found to be influenced by anthropogenic climate change. Additionally, soil moisture-temperature feedbacks were deemed important for the buildup of the exceptionally high temperatures. We quantify the relative role of both factors by applying the probabilistic event attribution framework and analyze ensemble simulations to distinguish the effect of climate change and the 2010 soil moisture conditions for annual maximum temperatures. The dry 2010 soil moisture alone has increased the risk of a severe heat wave in western Russia sixfold, while climate change from 1960 to 2000 has approximately tripled it. The combined effect of climate change and 2010 soil moisture yields a 13 times higher heat wave risk. We conclude that internal climate variability causing the dry 2010 soil moisture conditions formed a necessary basis for the extreme heat wave

A submonthly database for detecting changes in vegetation-atmosphere coupling

Land-atmosphere coupling and changes in coupling regimes are important for making precise future climate predictions and understanding vegetation-climate feedbacks. Here we introduce the Vegetation-Atmosphere Coupling (VAC) index which identifies regions and times of concurrent strong anomalies in temperature and photosynthetic activity. The different classes of the index determine whether a location is currently in an energy-limited or water-limited regime, and its high temporal resolution allows to investigate how these regimes change over time at the regional scale. We show that the VAC index helps to distinguish different evaporative regimes. It can therefore provide indirect information about the local soil moisture state. We further demonstrate how the index can be used to understand processes leading to and occurring during extreme climate events, using the 2010 heat wave in Russia and the 2010 Amazon drought as examples

Modeling land-climate coupling in Europe: Impact of land surface representation on climate variability and extremes

Land-climate coupling has been shown to be important for European summer climate variability and extreme events. However, the sensitivity of these feedbacks to land surface model (LSM) choice has been little investigated up to now. In this study, we assess the impact of the LSM on the simulated climate variability in a regional climate model (RCM). The experiments were conducted with the COSMO-CLM2RCM. COSMO-CLM2can be run with two alternative LSMs, the 2nd-generation LSM TERRA_ML or the more sophisticated 3rd-generation LSM Community Land Model (CLM3.5). The analyzed simulations include control and sensitivity experiments with prescribed soil moisture (dry or wet). Using CLM3.5 instead of TERRA_ML improves the simulated temperature variability by alleviating an overestimation of temperature inter-annual variability in the RCM. Also, the representation of the probability density functions of daily maximum summer temperature is improved when using the more advanced LSM. The reduced climate variability is linked to a larger ground heat flux and smaller variability in soil moisture and short-wave radiation. The latter effect results from the coupling of the LSM to the atmospheric module. In addition, using CLM3.5 reduces the sensitivity of COSMO-CLM2to extreme soil moisture conditions. An analysis assessing the relationship between the standard precipitation index and the subsequent number of hot days in summer reveals a better representation of this relationship using CLM3.5. Hence, we find that biases in climate variability and extremes can be reduced and the representation of land-climate coupling can be improved with the use of the more sophisticated LSM