Harmonic propagation of variability in surface energy balance within a coupled soil-vegetation-atmosphere system

International audienceThe response of a soil-vegetation-atmosphere continuum model to incoming radiation forcing is investigated in order to gain insights into the coupling of soil and atmospheric boundary layer (ABL) states and fluxes. The response is characterized through amplitude and phase propagation of the harmonics in order to differentiate between the response of the system to forcing at different frequencies (daily to hourly to near instantaneous). Stochastic noise is added to the surface energy balance. The amplitude of the noise is maximum at midday when the incoming radiative forcing is also at its peak. The temperatures and turbulent heat fluxes are shown to act as low-pass filters of the incoming radiation or energy budget noise variability at the surface. Conversely, soil heat flux is shown to act as a high-pass filter because of the strong contrast in the soil and air heat capacities and thermal conductivities. As a consequence, heat diffusion formulations that numerically dampen such forcing are potentially incapable of representing rapid fluctuations in soil heat flux (=30 min) and therefore introduce errors in the land-surface energy partitioning. The soil-vegetation-ABL continuum model and an electrical analogy for it are used to explain the frequency-dependent differences in the relative effectiveness of turbulent heat fluxes versus ground heat flux in dissipating noise in radiative forcing. Copyright 2011 by the American Geophysical Union

SMLFire1.0: a stochastic machine learning (SML) model for wildfire activity in the western United States

The annual area burned due to wildfires in the western United States (WUS) increased by more than 300 % between 1984 and 2020. However, accounting for the nonlinear, spatially heterogeneous interactions between climate, vegetation, and human predictors driving the trends in fire frequency and sizes at different spatial scales remains a challenging problem for statistical fire models. Here we introduce a novel stochastic machine learning (SML) framework, SMLFire1.0, to model observed fire frequencies and sizes in 12 km x 12 km grid cells across the WUS. This framework is implemented using Mixture Density Networks trained on a wide suite of input predictors. The modeled WUS fire frequency corresponds well with observations at both monthly (r = 0.94) and annual (r = 0.85) timescales, as do the monthly (r = 0.90) and annual (r = 0.88) area burned. Moreover, the annual time series of both fire variables exhibit strong correlations (r &ge; 0.6) in 16 out of 18 ecoregions. Our ML model captures the interannual variability and the distinct multidecade increases in annual area burned for both forested and non-forested ecoregions. Evaluating predictor importance with Shapley additive explanations, we find that fire month vapor pressure deficit (VPD) is the dominant driver of fire frequencies and sizes across the WUS, followed by 1000-hour dead fuel moisture (FM1000), total monthly precipitation (Prec), mean daily maximum temperature (Tmax), and fraction of grassland cover in a grid cell. Our findings serve as a promising use case of ML techniques for wildfire prediction in particular and extreme event modeling more broadly. They also highlight the power of ML driven parameterizations for potential implementation in the fire modules of Dynamic Global Vegetation Models (DGVMs) and Earth System Models (ESMs).</p

Machine-learned cloud classes from satellite data for process-oriented climate model evaluation

Clouds play a key role in regulating climate change but are difficult to simulate within Earth system models (ESMs). Improving the representation of clouds is one of the key tasks towards more robust climate change projections. This study introduces a new machine-learning based framework relying on satellite observations to improve understanding of the representation of clouds and their relevant processes in climate models. The proposed method is capable of assigning distributions of established cloud types to coarse data. It facilitates a more objective evaluation of clouds in ESMs and improves the consistency of cloud process analysis. The method is built on satellite data from the MODIS instrument labelled by deep neural networks with cloud types defined by the World Meteorological Organization (WMO), using cloud type labels from CloudSat as ground truth. The method is applicable to datasets with information about physical cloud variables comparable to MODIS satellite data and at sufficiently high temporal resolution. We apply the method to alternative satellite data from the Cloud\_cci project (ESA Climate Change Initiative), coarse-grained to typical resolutions of climate models. The resulting cloud type distributions are physically consistent and the horizontal resolutions typical of ESMs are sufficient to apply our method. We recommend outputting crucial variables required by our method for future ESM data evaluation. This will enable the use of labelled satellite data for a more systematic evaluation of clouds in climate models.Comment: Main Paper 16 pages, 11 figures. Supporting material 7 Pages, 8 figures. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Role of surface friction on shallow nonprecipitating convection

The role of surface friction on shallow nonprecipitating convection is investigated using a series of large-eddy simulations with varying surface friction velocity and with a cloud identification algorithm. As surface friction intensifies, convective rolls dominate over convective cells and secondary overturning circulation becomes stronger in the subcloud layer, thus transporting more moisture upward and more heat downward between the subcloud and cloud layers. Identifying individual clouds, using the identification algorithm based on a three-dimensional topological analysis, reveals that intensified surface friction increases the number of clouds and the degree of tilting in the downstream direction. Highly intensified surface friction increases wind shear across the cloud base and induces cloud tilting, which leads to a vertically parabolic profile of liquid water mixing ratio instead of the classical two-layer structure (conditionally unstable and trade inversion layers). Furthermore, cloud tilting induces more cloud cover and more cloud mass flux much above the cloud base (e.g. 0.8 km 1.2 km) because of increased lateral entrainment rate. Similarly, profiles of directly measured entrainment and detrainment rates show that detrainment in the lower cloud layer becomes smaller with stronger surface friction

On the generalization of agricultural drought classification from climate data

Climate change is expected to increase the likelihood of drought events, with severe implications for food security. Unlike other natural disasters, droughts have a slow onset and depend on various external factors, making drought detection in climate data difficult. In contrast to existing works that rely on simple relative drought indices as ground-truth data, we build upon soil moisture index (SMI) obtained from a hydrological model. This index is directly related to insufficiently available water to vegetation. Given ERA5-Land climate input data of six months with land use information from MODIS satellite observation, we compare different models with and without sequential inductive bias in classifying droughts based on SMI. We use PR-AUC as the evaluation measure to account for the class imbalance and obtain promising results despite a challenging time-based split. We further show in an ablation study that the models retain their predictive capabilities given input data of coarser resolutions, as frequently encountered in climate models

Evaluation of a simple approach for crop evapotranspiration partitioning and analysis of the water budget distribution for several crop species

International audienceClimate variability and climate change induce important intra- and inter-annual variability of precipitation that significantly alters the hydrologic cycle. The surface water budgets and the plant or ecosystem water use efficiency (WUE) are in turn modified. Obtaining greater insight into how climatic variability and agricultural practices affect water budgets and regarding their components in croplands is, thus, important for adapting crop management and limiting water losses. Therefore, the principal objectives of this study are: (1) to assess the contribution of different components to the agro-ecosystem water budget and (2) to evaluate how agricultural practices and climate modify the components of the surface water budget. To achieve these goals, we tested a new method for partitioning evapotranspiration (ETR), measured by means of an eddy-covariance method, into soil evaporation (E) and plant transpiration (TR) based on marginal distribution sampling (MDS). The partitioning method proposed requires continuous flux recording and measurements of soil temperature and humidity close to the surface, global radiation above the canopy and assessment of leaf area index dynamics. This method is well suited for crops because it requires a dataset including long bare-soil periods alternating with vegetated periods for accurate partitioning estimation. We compared these estimations with calibrated simulations of the ICARE-SVAT double source mechanistic model. The results showed good agreement between the two partitioning methods, demonstrating that MDS is a convenient, simple and robust tool for estimating E with reasonable associated uncertainties. During the growing season, the proportion of E in ETR was approximately one-third and varied mainly with crop leaf area. When calculated on an annual time scale, the proportion of E in ETR reached more than 50%, depending on the crop leaf area and on the duration and distribution of bare soil within the year

Global downscaling of remotely sensed soil moisture using neural networks

Characterizing soil moisture at spatiotemporal scales relevant to land surface processes (i.e., of the order of 1&thinsp;km) is necessary in order to quantify its role in regional feedbacks between the land surface and the atmospheric boundary layer. Moreover, several applications such as agricultural management can benefit from soil moisture information at fine spatial scales. Soil moisture estimates from current satellite missions have a reasonably good temporal revisit over the globe (2–3-day repeat time); however, their finest spatial resolution is 9&thinsp;km. NASA's Soil Moisture Active Passive (SMAP) satellite has estimated soil moisture at two different spatial scales of 36 and 9&thinsp;km since April 2015. In this study, we develop a neural-network-based downscaling algorithm using SMAP observations and disaggregate soil moisture to 2.25&thinsp;km spatial resolution. Our approach uses the mean monthly Normalized Differenced Vegetation Index (NDVI) as ancillary data to quantify the subpixel heterogeneity of soil moisture. Evaluation of the downscaled soil moisture estimates against in situ observations shows that their accuracy is better than or equal to the SMAP 9&thinsp;km soil moisture estimates.</p

Water, Energy, and Carbon with Artificial Neural Networks (WECANN): a statistically based estimate of global surface turbulent fluxes and gross primary productivity using solar-induced fluorescence

A new global estimate of surface turbulent fluxes, latent heat flux (LE) and sensible heat flux (H), and gross primary production (GPP) is developed using a machine learning approach informed by novel remotely sensed solar-induced fluorescence (SIF) and other radiative and meteorological variables. This is the first study to jointly retrieve LE, H, and GPP using SIF observations. The approach uses an artificial neural network (ANN) with a target dataset generated from three independent data sources, weighted based on a triple collocation (TC) algorithm. The new retrieval, named Water, Energy, and Carbon with Artificial Neural Networks (WECANN), provides estimates of LE, H, and GPP from 2007 to 2015 at 1°  ×  1° spatial resolution and at monthly time resolution. The quality of ANN training is assessed using the target data, and the WECANN retrievals are evaluated using eddy covariance tower estimates from the FLUXNET network across various climates and conditions. When compared to eddy covariance estimates, WECANN typically outperforms other products, particularly for sensible and latent heat fluxes. Analyzing WECANN retrievals across three extreme drought and heat wave events demonstrates the capability of the retrievals to capture the extent of these events. Uncertainty estimates of the retrievals are analyzed and the interannual variability in average global and regional fluxes shows the impact of distinct climatic events – such as the 2015 El Niño – on surface turbulent fluxes and GPP

The Impact of Anthropogenic Land Use and Land Cover Change on Regional Climate Extremes

Recent research highlights the role of land surface processes in heat waves, droughts, and other extreme events. Here we use an earth system model (ESM) from the Geophysical Fluid Dynamics Laboratory (GFDL) to investigate the regional impacts of historical anthropogenic land useland cover change (LULCC) on combined extremes of temperature and humidity. A bivariate assessment allows us to consider aridity and moist enthalpy extremes, quantities central to human experience of near-surface climate conditions. We show that according to this model, conversion of forests to cropland has contributed to much of the upper central US and central Europe experiencing extreme hot, dry summers every 2-3 years instead of every 10 years. In the tropics, historical patterns of wood harvesting, shifting cultivation and regrowth of secondary vegetation have enhanced near surface moist enthalpy, leading to extensive increases in the occurrence of humid conditions throughout the tropics year round. These critical land use processes and practices are not included in many current generation land models, yet these results identify them as critical factors in the energy and water cycles of the midlatitudes and tropics