Land Atmosphere Coupling Research Articles

Assimilating synthetic land surface temperature in a coupled land–atmosphere model

AbstractA realistic simulation of the atmospheric boundary layer (ABL) depends on an accurate representation of the land–atmosphere coupling. Land surface temperature (LST) plays an important role in this context and the assimilation of LST can lead to improved estimates of the boundary layer and its processes. We assimilated synthetic satellite LST retrievals derived from a nature run as truth into a fully coupled, state‐of‐the‐art land–atmosphere numeric weather prediction model. As assimilation system a local ensemble transform Kalman filter was used and the control vector was augmented by the soil temperature and humidity. To evaluate the concept of the augmented control vector, two‐day case‐studies with different control vector settings were conducted for clear‐sky periods in March and August 2017. These experiments with hourly LST assimilation were validated against the nature run and overall, the RMSE of atmospheric and soil temperature of the first‐guess (and analysis) were reduced. The temperature estimate of the ABL was particularly improved during daytime as was the estimate of the soil temperature during the whole diurnal cycle. The best impact of LST assimilation on the soil and the ABL was achieved with the augmented control vector. Through the coupling between the soil and the atmosphere, the assimilation of LST can have a positive impact on the temperature forecast of the ABL even after 15 hr because of the memory of the soil. These encouraging results motivate further work towards the assimilation of real satellite LST retrievals.

Refining projected multidecadal hydroclimate uncertainty in East-Central Europe using CMIP5 and single-model large ensemble simulations

Future hydroclimate projections of global climate models for East-Central Europe diverge to a great extent, thus, constrain adaptation strategies. To reach a more comprehensive understanding of this regional spread in model projections, we make use of the CMIP5 multi-model ensemble and six single-model initial condition large ensemble (SMILE) simulations to separate the effects of model structural differences and internal variability, respectively, on future hydroclimate projection uncertainty. To account for model uncertainty, we rank 32 CMIP5 models based on their predictive skill in reproducing multidecadal past hydroclimate variability. Specifically, we compare historical model simulations to long instrumental and reanalysis surface temperature and precipitation records. The top 3–ranked models—that best reproduce regional past multidecadal temperature and precipitation variability—show reduced spread in their projected future precipitation variability indicating less dry summer and wetter winter conditions in part at odds with previous expectations for Central Europe. Furthermore, not only does the regionally best performing CMIP5 models belong to the previously identified group of models with more realistic land-atmosphere interactions, their future summer precipitation projections also emerge from the range of six SMILEs’ future simulations. This suggests an important role for land-atmosphere coupling in regulating hydroclimate uncertainty on top of internal variability in the upcoming decades. Our results help refine the relative contribution of structural differences between models in affecting future hydroclimate uncertainty in the presence of irreducible internal variability in East-Central Europe.

Impact of Deforestation on Land–Atmosphere Coupling Strength and Climate in Southeast Asia

Southeast Asia (SEA) is a deforestation hotspot. A thorough understanding of the accompanying biogeophysical consequences is crucial for sustainable future development of the region’s ecosystem functions and society. In this study, data from ERA-Interim driven simulations conducted with the state-of-the-art regional climate model COSMO-CLM (CCLM; version 4.8.17) at 14 km horizontal resolution are analyzed over SEA for the period from 1990 to 2004, and during El Niño–Southern Oscillation (ENSO) events for November to March. A simulation with large-scale deforested land cover is compared to a simulation with no land cover change. In order to attribute the differences due to deforestation to feedback mechanisms, the coupling strength concept is applied based on Pearson correlation coefficients. The correlations were calculated based on 10-day means between the latent heat flux and maximum temperature, the latent and sensible heat flux, and the latent heat flux and planetary boundary layer height. The results show that the coupling strength between land and atmosphere increased for all correlations due to deforestation. This implies a strong impact of the land on the atmosphere after deforestation. Differences in environmental conditions due to deforestation are most effective during La Niña years. The strength of La Nina events on the region is reduced as the impact of deforestation on the atmosphere with drier and warmer conditions superimpose this effect. The correlation strength also intensified and shifted towards stronger coupling during El Niño events for both Control and Grass simulations. However, El Niño years have the potential to become even warmer and drier than during usual conditions without deforestation. This could favor an increase in the formation of tropical cyclones. Whether deforestation will lead to a permanent transition to agricultural production increases in this region cannot be concluded. Rather, the impact of deforestation will be an additional threat besides global warming in the next decades due to the increase in the occurrence of multiple extreme events. This may change the type and severity of upcoming impacts and the vulnerability and sustainability of our society.

Warm‐season precipitation drivers in northeastern Argentina: Diurnal cycle of the atmospheric moisture balance and land–atmosphere coupling

AbstractSoutheastern South America is influenced by moisture transport from lower latitudes, sustains intense convective storms and is a land–atmosphere coupling hotspot, but the interconnection between these processes is still not well understood. We present the warm‐season diurnal cycle climatology of the atmospheric water balance components in the South American Low‐Level Jet (SALLJ) exit region in northeastern Argentina during 1998–2012. Different precipitation‐based types of events (clear‐sky and rainy days) were explored together with processes tied to the land–atmosphere coupling at the daily scale. Our research was based on simulations with and without soil moisture–atmosphere coupling with the RCA4 regional climate model. A control simulation was compared with a sensitivity simulation where the soil moisture was prescribed with the daily climatological values from the control run. The ERA5 reanalysis and the satellite precipitation products TRMM‐3B42 v7 and CMORPH v1.0 bias‐corrected were used for comparative purposes. From the diurnal water balance analysis, we found that moisture flux convergence in the region is the main driver for nocturnal precipitation while local evapotranspiration feeds afternoon rain events. Rainy afternoons do not show differences between simulations, but rainy nights seem to be affected. Moreover, daily correlations between surface and boundary‐layer variables showed that the local coupling is weaker during rainy days than during clear‐sky days. Therefore, we suggest that changes in non‐local drivers, such as the moisture flux through the SALLJ, are more relevant for rainy nights than the local coupling.

Effect of Sea-ice Drift on the Onset of Snowball Climate on Rapidly Rotating Aqua-planets

Abstract Previous studies have shown that sea-ice drift effectively promotes the onset of a globally ice-covered snowball climate for paleo Earth and for tidally locked planets around low-mass stars. Here, we investigate whether sea-ice drift can influence the stellar flux threshold for a snowball climate onset on rapidly rotating aqua-planets around a Sun-like star. Using a fully coupled atmosphere–land–ocean–sea-ice model and turning sea-ice drift on or off, circular orbits with no eccentricity (e = 0) and an eccentric orbit (e = 0.2) are examined. When sea-ice drift is turned off, the stellar flux threshold for the snowball onset is 1250–1275 and 1173–1199 W m−2 for e = 0 and 0.2, respectively. The difference is mainly due to the poleward retreat of sea ice and snow edges when the planet is close to the perihelion in the eccentric orbit. When sea-ice drift is turned on, the respective stellar flux threshold is 1335–1350 and 1250–1276 W m−2. This means that sea-ice drift increases the snowball onset threshold by ≈80 W m−2 for both e = 0 and 0.2, promoting the formation of a snowball climate state. We further show that oceanic dynamics have a small effect, ≤26 W m−2, on the snowball onset threshold. This is because oceanic heat transport becomes weaker and weaker as the sea-ice edge is approaching the equator. These results imply that sea-ice dynamics are important for the climate of planets close to the outer edge of the habitable zone, but oceanic heat transport is less important.

Improving Near-Surface Short-Range Weather Forecasts Using Strongly Coupled Land–Atmosphere Data Assimilation with GSI-EnKF

Abstract Strongly coupled land–atmosphere data assimilation has not yet been implemented into operational numerical weather prediction (NWP) systems. Up to now, upper-air measurements have been assimilated mainly in atmospheric analyses, while land and near-surface data have been assimilated mainly into land surface models. Thus, this study aims to explore the benefits of assimilating atmospheric and land surface observations within the framework of strongly coupled data assimilation. Specifically, we added soil moisture as a control state within the ensemble Kalman filter (EnKF)-based Gridpoint Statistical Interpolation (GSI) and conducted a series of numerical experiments through the assimilation of 2-m temperature/humidity and in situ surface soil moisture data along with conventional atmospheric measurements such as radiosondes into the Weather Research and Forecasting (WRF) Model with the Noah land surface model. The verification against in situ measurements and analyses show that compared to the assimilation of conventional data, adding soil moisture as a control state and assimilating 2-m humidity can bring additional benefits to analyses and forecasts. The impact of assimilating 2-m temperature (surface soil moisture) data is positive mainly on the temperature (soil moisture) analyses but on average marginal for other variables. On average, below 750 hPa, verification against the NCEP analysis indicates that the respective RMSE reduction in the forecasts of temperature and humidity is 5% and 2% for assimilating conventional data; 10% and 5% for including soil moisture as a control state; and 16% and 11% for simultaneously adding soil moisture as a control state and assimilating 2-m humidity data.

The GLACE-Hydrology Experiment: Effects of Land–Atmosphere Coupling on Soil Moisture Variability and Predictability

AbstractThe impact of land–atmosphere anomaly coupling on land variability is investigated using a new two-stage climate model experimental design called the “GLACE-Hydrology” experiment. First, as in the GLACE-CMIP5 experiment, twin sets of coupled land–atmosphere climate model (CAM5-CLM4.5) ensembles are performed, with each simulation using the same prescribed observed sea surface temperatures and radiative forcing for the years 1971–2014. In one set, land–atmosphere anomaly coupling is removed by prescribing soil moisture to follow the control model’s seasonally evolving soil moisture climatology (“land–atmosphere uncoupled”), enabling a contrast with the original control set (“land–atmosphere coupled”). Then, the atmospheric outputs from both sets of simulations are used to force land-only ensemble simulations, allowing investigation of the resulting soil moisture variability and memory under both the coupled and uncoupled scenarios. This study finds that in midlatitudes during boreal summer, land–atmosphere anomaly coupling significantly strengthens the relationship between soil moisture and evapotranspiration anomalies, both in amplitude and phase. This allows for decreased moisture exchange between the land surface and atmosphere, increasing soil moisture memory and often its variability as well. Additionally, land–atmosphere anomaly coupling impacts runoff variability, especially in wet and transition regions, and precipitation variability, although the latter has surprisingly localized impacts on soil moisture variability. As a result of these changes, there is an increase in the signal-to-noise ratio, and thereby the potential seasonal predictability, of SST-forced hydroclimate anomalies in many areas of the globe, especially in the midlatitudes. This predictability increase is greater for soil moisture than precipitation and has important implications for the prediction of drought.

Understanding the linkage between soil moisture variability and temperature extremes over the Indian region

Soil moisture (SM) and near-surface temperature variations are known to be linked through land–atmosphere interactions. While previous investigators have examined the association between temperature extremes and large-scale atmospheric circulation variability, the role of land–atmosphere coupling on temperature extremes over the monsoon-dominated region of India is not well understood. This study presents an analysis of hydro-meteorological datasets for the 67-year period (1948–2014) to assess the impact of long-term soil moisture changes on temperature extremes over the Indian region. Firstly, our findings show that the hot-spot of land–atmosphere coupling located across north-central India (NCI) is a region where SM variations can significantly influence temperature extremes. We further note that the NCI region experienced a significant declining trend in SM (about 1.1 mm/decade) during 1948–2014, in association with the decreasing trend of monsoon precipitation. Our findings suggest that the long-term decrease of SM over the NCI has favored increased incidence of temperature extremes through strengthening (weakening) of sensible (latent) heat fluxes; while the loss of soil moisture memory has additionally promoted increased variability of temperature extremes. The frequency, duration and variability of extreme temperatures are found to increase significantly by 1–2 occurrences, 5–6 days and 43%, respectively, in association with a decrease of 10 mm SM over NCI. The Generalized Extreme Value (GEV) distributions are fitted to extreme temperature duration (ExTD) using SM as a covariate to quantify the role of SM on temperature extremes over NCI. GEV analysis reveals that drier SM conditions (10th percentile) lead to an increase in the 67-year return value of ExTD by 9–10 days, relative to wet SM conditions (90th percentile) over NCI. Furthermore, it is interesting to note that the rise in temperature extremes over NCI in the recent three decades has been more prominent during the monsoon and post-monsoon seasons as compared to the pre-monsoon months.

Projected changes in the terrestrial and oceanic regulators of climate variability across sub-Saharan Africa

Future changes in the sign and intensity of ocean–land–atmosphere interactions have been insufficiently studied, despite implications for regional climate change projections, extreme event statistics, and seasonal climate predictability. In response to this deficiency, the present study focuses on projected responses to the enhanced greenhouse effect in: (1) the mean state of the atmosphere and land surface; (2) oceanic and terrestrial drivers of sub-Saharan climate variability; and (3) total seasonal climate predictability of sub-Saharan Africa, a region known for its pronounced land–atmosphere coupling. Analysis focuses on output from 23 Earth System Models in the Coupled Model Intercomparison Project Phase Five for the late twentieth and twenty-first centuries. It is projected that the greatest warming across sub-Saharan Africa will occur over the Sahel, the monsoon season will become more persistent into late summer and autumn, short rains in the Horn of Africa (HOA) will intensify, and leaf area index will increase across the HOA. Stepwise Generalized Equilibrium Feedback Assessment, i.e. a multivariate statistical approach, is applied to the model output over sub-Saharan Africa in order to explore the oceanic and terrestrial drivers of regional climate. The models indicate that the study region’s climate variability is dominated by oceanic drivers, with secondary contributions from soil moisture and very modest impacts from vegetation. Overall, the general model consensus of future projections indicates a concerning diminished seasonal predictability of sub-Saharan African regional climate based on key oceanic and terrestrial predictors and an elevated role of the land surface (associated with soil moisture anomalies) compared to oceanic drivers in regulating regional climate variability.

A tale of two futures: contrasting scenarios of future precipitation for West Africa from an ensemble of regional climate models

The results of a large ensemble of regional climate models lead to two contrasting but plausible scenarios for the precipitation change over West Africa, one where mean precipitation is projected to decrease significantly over the Gulf of Guinea in spring and the Sahel in summer, and the other where summer precipitation over both regions is projected to increase.Dry and wet models show similar patterns of the dynamic and thermodynamic terms of the moisture budget, although their magnitudes are larger in the dry models. The largest discrepancies are found in the strength of the land-atmosphere coupling, with dry models showing a marked decrease in soil moisture and evapotranspiration.Some changes in precipitation characteristics are consistent for both sets of models. In particular, precipitation frequency is projected to decrease in spring over the Gulf of Guinea and in summer over the Sahel, but precipitation is projected to become more intense.

Analysis of local land-atmosphere coupling in rainy season over a typical underlying surface in Tibetan Plateau based on field measurements and ERA5

This study investigates the local land-atmosphere coupling (LoCo) in the rainy season by applying a mixing diagram method to the field measurements and ERA5 data over a typical underlying surface in Tibetan Plateau (TP). Comparison of frequencies of meteorological variables in June, July, August, and September (JJAS) in 2011 in the two datasets suggests that the ranges of meteorological variables are similar although the ERA5 shows a cold and wet bias. The mixing diagram analysis on 12 July, and 7–8 August 2011 calculated based on the two datasets reveals that the ERA5 could provide similar curves of the mixing diagram in the sunny days, although it tends to overestimate surface sensible and latent heat fluxes (Hsfc, LEsfc), and entrainment sensible heat fluxes (Hent), underestimate entrainment latent heat fluxes (LEent) in sunny days. The mixing diagram analysis under four different weather conditions based on ERA5 shows that LoCo behaves differently under different weather conditions and that as cloud amount increases, less heat and more water vapor are transported into the PBL from the land surface. Study of the monthly mean mixing diagram in JJAS from 2011 to 2014 based on the two datasets indicates the curves of the monthly-mean Cp*θ and Lv*q and PBL energy budgets exhibit a monthly variation, which relates to Southern Asian monsoon (ASM). Discussion of the relationship between mean Hsfc and max PBLH in the daytime reveals that the role of Hsfc in PBL growth at the daily scale is weakest in July due to the impact of ASM.

Physical and Optical Properties of Ultra-black Nickel–Phosphorus for a Total Solar Irradiance Measurement

Abstract The total solar irradiance at the top of the atmosphere is the primary source of energy of the Earth’s highly coupled atmosphere–land–ocean system. Small fluctuations of the solar flux density in scales from years to millennia could impact the energy balance of this system due to nonlinear effects. The quantification of this variability depends on absolute radiometers on board of space-based platforms. Although there has been significant improvement in the design and calibration of absolute radiometers during the last decades, the uncertainties in the measurements have not allowed us to untangle the natural and anthropogenic drivers of the observed changes of the climatic patterns appropriately. One of the critical components of the absolute radiometers is the coating of the sensor elements, which should absorb the radiation efficiently. Here we discuss the optical characteristics of ultra-black Nickel–Phosphorus (Ni–P) and its relations with the surface morphology. The ultra-black Ni–P has important unique properties such as low reflectance and uniformity of deposition in complex geometries. Ni–P multilayer was deposited by electroless on aluminum substrates. The surface was etched by oxidizing acid to produce ultra-black Ni–P. Characterization techniques were used to describe the properties of the material. We describe the directional reflectance employing the bidirectional reflectance distribution function. Additionally, we used reflectance maps to show the influence of the pores on the reflectance. Ultra-black Ni–P exhibited a high absorptance and dependence with the light incidence angle. Based on the results, the material demonstrated the opportunity of many terrestrial and space applications as a black coating absorber.

Using CESM-RESFire to understand climate–fire–ecosystem interactions and the implications for decadal climate variability

Abstract. Large wildfires exert strong disturbance on regional and global climate systems and ecosystems by perturbing radiative forcing as well as the carbon and water balance between the atmosphere and land surface, while short- and long-term variations in fire weather, terrestrial ecosystems, and human activity modulate fire intensity and reshape fire regimes. The complex climate–fire–ecosystem interactions were not fully integrated in previous climate model studies, and the resulting effects on the projections of future climate change are not well understood. Here we use the fully interactive REgion-Specific ecosystem feedback Fire model (RESFire) that was developed in the Community Earth System Model (CESM) to investigate these interactions and their impacts on climate systems and fire activity. We designed two sets of decadal simulations using CESM-RESFire for present-day (2001–2010) and future (2051–2060) scenarios, respectively, and conducted a series of sensitivity experiments to assess the effects of individual feedback pathways among climate, fire, and ecosystems. Our implementation of RESFire, which includes online land–atmosphere coupling of fire emissions and fire-induced land cover change (LCC), reproduces the observed aerosol optical depth (AOD) from space-based Moderate Resolution Imaging Spectroradiometer (MODIS) satellite products and ground-based AErosol RObotic NETwork (AERONET) data; it agrees well with carbon budget benchmarks from previous studies. We estimate the global averaged net radiative effect of both fire aerosols and fire-induced LCC at -0.59±0.52 W m−2, which is dominated by fire aerosol–cloud interactions (-0.82±0.19 W m−2), in the present-day scenario under climatological conditions of the 2000s. The fire-related net cooling effect increases by ∼170 % to -1.60±0.27 W m−2 in the 2050s under the conditions of the Representative Concentration Pathway 4.5 (RCP4.5) scenario. Such considerably enhanced radiative effect is attributed to the largely increased global burned area (+19 %) and fire carbon emissions (+100 %) from the 2000s to the 2050s driven by climate change. The net ecosystem exchange (NEE) of carbon between the land and atmosphere components in the simulations increases by 33 % accordingly, implying that biomass burning is an increasing carbon source at short-term timescales in the future. High-latitude regions with prevalent peatlands would be more vulnerable to increased fire threats due to climate change, and the increase in fire aerosols could counter the projected decrease in anthropogenic aerosols due to air pollution control policies in many regions. We also evaluate two distinct feedback mechanisms that are associated with fire aerosols and fire-induced LCC, respectively. On a global scale, the first mechanism imposes positive feedbacks to fire activity through enhanced droughts with suppressed precipitation by fire aerosol–cloud interactions, while the second one manifests as negative feedbacks due to reduced fuel loads by fire consumption and post-fire tree mortality and recovery processes. These two feedback pathways with opposite effects compete at regional to global scales and increase the complexity of climate–fire–ecosystem interactions and their climatic impacts.

Reconciling the disagreement between observed and simulated temperature responses to deforestation

Land use changes have great potential to influence temperature extremes. However, contradictory summer daytime temperature responses to deforestation are reported between observations and climate models. Here we present a pertinent comparison between multiple satellite-based datasets and climate model deforestation experiments. Observationally-based methods rely on a space-for-time assumption, which compares neighboring locations with contrasting land covers as a proxy for land use changes over time without considering possible atmospheric feedbacks. Offline land simulations or subgrid-level analyses agree with observed warming effects only when the space-for-time assumption is replicated. However, deforestation-related cloud and radiation effects manifest in coupled climate simulations and observations at larger scales, which show that a reduction of hot extremes with deforestation – as simulated in a number of CMIP5 models – is possible. Our study provides a design and analysis methodology for land use change studies and highlights the importance of including land-atmosphere coupling, which can alter deforestation-induced temperature changes.

A sensitivity study on the response of convection initiation to in situ soil moisture in the central United States

Soil moisture can affect precipitation, however there is significant debate about the sign and strength of land–atmosphere coupling. This may be due to the differences regarding the selection of data sources, the presence of atmospheric persistence, and the spatial and temporal scales of the analysis. This study is a sensitivity analysis that quantifies how each of these factors influences land–atmosphere coupling in the central United States from 2005 to 2007. Four different sources of soil moisture are used to represent soil moisture: in situ measurements and three satellite products. The Thunderstorm Observation by Radar (ThOR) algorithm is used to identify convective events. The results show that warm-season afternoon convection initiates preferentially over dry soil, if in situ measurements are used. However, when satellite data are used the sign and strength of the soil moisture-precipitation coupling varies depending on which satellite dataset is used. This demonstrates that evaluations of land–atmosphere coupling strength are sensitive to the source of soil moisture. Our results also show that accounting for atmospheric persistence tends to weaken apparent land–atmosphere coupling. Changing the spatial scale of the analysis can also influence the sign and coupling strength. Analyses that are performed at a coarser spatial resolution tend to indicate a wet preference because they do not capture the local negative feedbacks. This study demonstrates that contradictions in the literature regarding the sign and strength of soil moisture-precipitation feedbacks can be partly attributed to differences in datasets, methods and scale of the analysis.

Retrieving the global distribution of the threshold of wind erosion from satellite data and implementing it into the Geophysical Fluid Dynamics Laboratory land–atmosphere model (GFDL AM4.0/LM4.0)

Abstract. Dust emission is initiated when surface wind velocities exceed the threshold of wind erosion. Many dust models used constant threshold values globally. Here we use satellite products to characterize the frequency of dust events and land surface properties. By matching this frequency derived from Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue aerosol products with surface winds, we are able to retrieve a climatological monthly global distribution of the wind erosion threshold (Vthreshold) over dry and sparsely vegetated surfaces. This monthly two-dimensional threshold velocity is then implemented into the Geophysical Fluid Dynamics Laboratory coupled land–atmosphere model (AM4.0/LM4.0). It is found that the climatology of dust optical depth (DOD) and total aerosol optical depth, surface PM10 dust concentrations, and the seasonal cycle of DOD are better captured over the “dust belt” (i.e., northern Africa and the Middle East) by simulations with the new wind erosion threshold than those using the default globally constant threshold. The most significant improvement is the frequency distribution of dust events, which is generally ignored in model evaluation. By using monthly rather than annual mean Vthreshold, all comparisons with observations are further improved. The monthly global threshold of wind erosion can be retrieved under different spatial resolutions to match the resolution of dust models and thus can help improve the simulations of dust climatology and seasonal cycles as well as dust forecasting.

Modeled Land Atmosphere Coupling Response to Soil Moisture Changes with Different Generations of Land Surface Models

An idealized study with two land surface models (LSMs): TERRA-Multi Layer (TERRA-ML) and Community Land Model (CLM) alternatively coupled to the same atmospheric model COSMO (Consortium for Small-Scale Modeling), reveals differences in the response of the LSMs to initial soil moisture. The bulk parameterization of evapotranspiration pathways, which depends on the integrated soil moisture of active layers rather than on each discrete layer, results in a weaker response of the surface energy flux partitioning to changes in soil moisture for TERRA-ML, as compared to CLM. The difference in the resulting surface energy flux partitioning also significantly affects the model response in terms of the state of the atmospheric boundary layer. For vegetated land surfaces, both models behave quite differently for drier regimes. However, deeper reaching root fractions in CLM align both model responses with each other. In general, differences in the parameterization of the available root zone soil moisture, evapotranspiration pathways, and the soil-vegetation structure in the two LSMs are mainly responsible for the diverging tendencies of the simulated land atmosphere coupling responses.

Groundwater influence on soil moisture memory and land–atmosphere fluxes in the Iberian Peninsula

Abstract. Groundwater plays an important role in the terrestrial water cycle, interacting with the land surface via vertical fluxes through the water table and distributing water resources spatially via gravity-driven lateral transport. It is therefore essential to have a correct representation of groundwater processes in land surface models, as land–atmosphere coupling is a key factor in climate research. Here we use the LEAFHYDRO land surface and groundwater model to study the groundwater influence on soil moisture distribution and memory, and evapotranspiration (ET) fluxes in the Iberian Peninsula over a 10-year period. We validate our results with time series of observed water table depth from 623 stations covering different regions of the Iberian Peninsula, showing that the model produces a realistic water table, shallower in valleys and deeper under hilltops. We find patterns of shallow water table and strong groundwater–land surface coupling over extended interior semi-arid regions and river valleys. We show a strong seasonal and interannual persistence of the water table, which induces bimodal memory in the soil moisture fields; soil moisture “remembers” past wet conditions, buffering drought effects, and also past dry conditions, causing a delay in drought recovery. The effects on land–atmosphere fluxes are found to be significant: on average over the region, ET is 17.4 % higher when compared with a baseline simulation with LEAFHYDRO's groundwater scheme deactivated. The maximum ET increase occurs in summer (34.9 %; 0.54 mm d−1). The ET enhancement is larger over the drier southern basins, where ET is water limited (e.g. the Guadalquivir basin and the Mediterranean Segura basin), than in the northern Miño/Minho basin, where ET is more energy limited than water limited. In terms of river flow, we show how dry season baseflow is sustained by groundwater originating from accumulated recharge during the wet season, improving significantly on a free-drain approach, where baseflow comes from water draining through the top soil, resulting in rivers drying out in summer. Convective precipitation enhancement through local moisture recycling over the semi-arid interior regions and summer cooling are potential implications of these groundwater effects on climate over the Iberian Peninsula. Fully coupled land surface and climate model simulations are needed to elucidate this question.

A national-scale drought assessment in Uganda based on evapotranspiration deficits from the Bouchet hypothesis

For effective management of drought risks, a process-specific indicator needs to be employed rather than depending only on precipitation anomalies. In this study, we performed an evapotranspiration-based (ET-based) assessment of agricultural droughts in Uganda where drought has usually been assessed with precipitation deficiency. The land-surface ET and evaporative demand were estimated with a state-of-the-art complementary principle across the country under poor ET data availability. Using the ET estimates, the Standardized Evapotranspiration Deficit Index (SEDI) was quantified to assess agricultural droughts for 1984–2017. The ET-based drought assessment was compared with conventional precipitation-based analyses using the Standardized Precipitation Index (SPI) in respect of severity, areal extent, and temporal trends. Results showed that the complementary principle provided acceptable performance in reproducing interpolated latent flux observations. SPI and SEDI were strongly correlated, and thus yielded identical temporal variations of mean drought severity and areal extent. The trend maps of SPI and SEDI also tended to agree, and indicate that central and northwestern Uganda would face increasing drought risks in the future. The declining precipitation in Uganda has been attributed to rising surface temperatures of the Indian Ocean, and is expected to persist up to the next decade. This study suggests that droughts in major croplands could be often accompanied by high heat-wave temperatures in Uganda due to strong land-atmosphere coupling, and thus mortality risks in ecosystems could increase together with water stress.

Assessing the sensitivity of land-atmosphere coupling strength to boundary and surface layer parameters in the WRF model over Amazon

Abstract Modeling tools can be used to diagnose regional land-atmosphere (L-A) coupling strength in the absence of sufficient observations, but subject to uncertainties associated with parameters in model physical parameterizations. Different sensitivity analysis (SA) approaches may lead to different conclusions about the underlying sensitivities. In this study, we quantify simulation uncertainties related to parameter perturbations, and use different approaches to conduct parameter SA on the WRF model pertaining to L-A coupling strength for simulations over the Amazon region. A total of twenty parameters from the Yonsei University (YSU) planetary boundary layer (PBL) and the revised MM5 surface layer (SL) schemes were selected in this analysis. Three different SA methods, the Morris One-at-A-Time (MOAT) method, the Multivariate Adaptive Regression Splines (MARS) method, and the Sobol’ method, were employed to analyze seven WRF-simulated variables and five L-A coupling metrics. Results show that 1) parameter perturbations cause large simulation uncertainties which are comparable to those in the observations; 2) three different SA methods give consistent L-A coupling strength outcomes; 3) six out of the twenty parameters contribute 80%–95% of the total variance in the metrics analyzed, and first-order effects dominate over interaction effects; 4) the twelve variables/metrics of interest show similar sensitivity patterns to the selected parameters, which is consistent across all the methods used. Physical mechanisms for how the sensitive parameters act in determining the L-A coupling strength and associated variables also are illustrated. Our results will help quantifying L-A coupling strength and establishing a basis for parameter calibration over the Amazon region.