Variability Of Atmospheric Forcing Research Articles

Влияние короткопериодных вариаций атмосферных воздействий на крупномасштабную структуру океанографических полей

The dependence of the large-scale structure of oceanographic fields on short-term variations in atmospheric forcing (AF) at the ocean surface has been studied. Estimates of this dependence were obtained by comparing the results of two numerical experiments with the NEMO model in the ORCA1 configuration used as a computational core in the data assimilation system at the Hydrometeorological Center of Russia. The experiments similar in all other respects differed only in the discreteness of the AF data: 3–24 hours in the main experiment and 1 month in the experiment with time-averaged AF. Large-scale characteristics were compared: kinetic energy averaged over large regions, Atlantic Meridional Overturning Circulation, meridional heat transport, mixed layer depth, sea ice volume. It is shown that neglecting high-frequency AF variability can significantly distort the long-term evolution of oceanographic fields reproduced by ocean models and thereby affect the quality of model forecasts. Keywords: oceanographic fields, large-scale structure, NEMO model, atmospheric forcing, short-term variations

Hybrid forecasting: blending climate predictions with AI models

Abstract. Hybrid hydroclimatic forecasting systems employ data-driven (statistical or machine learning) methods to harness and integrate a broad variety of predictions from dynamical, physics-based models – such as numerical weather prediction, climate, land, hydrology, and Earth system models – into a final prediction product. They are recognized as a promising way of enhancing the prediction skill of meteorological and hydroclimatic variables and events, including rainfall, temperature, streamflow, floods, droughts, tropical cyclones, or atmospheric rivers. Hybrid forecasting methods are now receiving growing attention due to advances in weather and climate prediction systems at subseasonal to decadal scales, a better appreciation of the strengths of AI, and expanding access to computational resources and methods. Such systems are attractive because they may avoid the need to run a computationally expensive offline land model, can minimize the effect of biases that exist within dynamical outputs, benefit from the strengths of machine learning, and can learn from large datasets, while combining different sources of predictability with varying time horizons. Here we review recent developments in hybrid hydroclimatic forecasting and outline key challenges and opportunities for further research. These include obtaining physically explainable results, assimilating human influences from novel data sources, integrating new ensemble techniques to improve predictive skill, creating seamless prediction schemes that merge short to long lead times, incorporating initial land surface and ocean/ice conditions, acknowledging spatial variability in landscape and atmospheric forcing, and increasing the operational uptake of hybrid prediction schemes.

Intraseasonal variability of the South Vietnam upwelling, South China Sea: influence of atmospheric forcing and ocean intrinsic variability

Abstract. The South Vietnam upwelling (SVU) develops off the Vietnamese coast (South China Sea, SCS) during the southwest summer monsoon over four main areas: the northern coastal upwelling (NCU), the southern coastal upwelling (SCU), the offshore upwelling (OFU) and the shelf off the Mekong River mouth (MKU). An ensemble of 10 simulations with perturbed initial conditions were run with the fine-resolution SYMPHONIE model (1 km inshore) to investigate the daily to intraseasonal variability of the SVU and the influence of the ocean intrinsic variability (OIV) during the strong SVU of summer 2018. The intraseasonal variability is similar for the SCU, MKU and OFU, driven to the first order by the wind variability. The MKU and SCU are induced by stable ocean dynamics (the northeastward then eastward boundary current) and have very little chaotic variability. The OIV has a stronger influence on OFU. In July, OFU mainly develops along the northern flank of the eastward jet. The influence of the OIV is strongest and related to the chaotic variability of the meridional position of the jet. In August, this position is stable and OFU develops mainly in the area of positive wind curl and cyclonic eddies north of the jet. The influence of the OIV, weaker than in July, is related to the organization of this mesoscale circulation. The NCU shows a behavior different from that observed in the other areas. In the heart of summer, a large-scale circulation formed by the eastward jet and eddy dipole is well established with an alongshore current preventing the NCU development. In early and late summer, this circulation is weaker, allowing a mesoscale circulation of strongly chaotic nature to develop in the NCU area. During those periods, the OIV influence on the NCU is very strong and related to the organization of this mesoscale circulation: the NCU is favored (annihilated) by offshore-oriented (alongshore) structures.

Wind and shipping influences on sea currents around an inshore fish farm in a heavily contested Mediterranean embayment

Marine aquaculture expansion will continue to be challenged by a lack of space in areas of the marine domain that can support aquaculture, due to competition from other maritime activities vying for the same spaces. This research attempts to characterise those natural and anthropogenic forces that influence and drive sea currents measured over a 16-month period around a nearshore fish farm located within a busy multiple-use bay in the central Mediterranean Sea. Evidence from a concomitant two-year-long meteorological dataset reveals the occurrence of variable winds that result in a dominant and perpetual forcing on near-surface current magnitude and direction. The correlation coefficient between wind and sea currents decreases with increasing depth and hourly time lag. Moreover, the observed water level variations were more related to meteorological forcing factors than to tidal influences recorded at the mouth of the bay. However, intermittently observed water current values could not be exclusively explained by atmospheric forcing variables when the relationship between in-situ measurements and sea current values predicted by the hydrodynamic-wave model (Marine Forecasting System) was analysed. Consequently, this lack of correlation spurred further analysis, which revealed that relevant water current disturbances, particularly in near-surface sea currents, corresponded to 131 different Automatic Identification System (AIS) records of vessels. These vessels included bunkering barges, pilot boats and dredging vessels operating and navigating within a 650 m radius from the fish farm and during a 10-min window. This study thus provides evidence for natural and anthropogenically-derived influences on local fish farm-scale hydrodynamics that have important implications for the effective and sustainable development of aquaculture within a marine spatial context, especially in congested, multi-use environments.

Causes and evolution of winter polynyas north of Greenland

Abstract. During the 42-year period (1979–2020) of satellite measurements, four major winter (December–March) polynyas have been observed north of Greenland: one in December 1986 and three in the last decade, i.e., February of 2011, 2017, and 2018. The 2018 polynya was unparalleled in its magnitude and duration compared to the three previous events. Given the apparent recent increase in the occurrence of these extreme events, this study aims to examine their evolution and causality, in terms of forced versus natural variability. The limited weather station and remotely sensed sea ice data are analyzed combining with output from the fully coupled Regional Arctic System Model (RASM), including one hindcast and two ensemble simulations. We found that neither the accompanying anomalous warm surface air intrusion nor the ocean below had an impact (i.e., no significant ice melting) on the evolution of the observed winter open-water episodes in the region. Instead, the extreme atmospheric wind forcing resulted in greater sea ice deformation and transport offshore, accounting for the majority of sea ice loss in all four polynyas. Our analysis suggests that strong southerly winds (i.e., northward wind with speeds greater than 10 m s−1) blowing persistently over the study region for at least 2 d or more were required over the study region to mechanically redistribute some of the thickest Arctic sea ice out of the region and thus to create open-water areas (i.e., a latent heat polynya). To assess the role of internal variability versus external forcing of such events, we carried out and examined results from the two RASM ensembles dynamically downscaled with output from the Community Earth System Model (CESM) Decadal Prediction Large Ensemble (DPLE) simulations. Out of 100 winters in each of the two ensembles (initialized 30 years apart: one in December 1985 and another in December 2015), 17 and 16 winter polynyas were produced north of Greenland, respectively. The frequency of polynya occurrence had no apparent sensitivity to the initial sea ice thickness in the study area pointing to internal variability of atmospheric forcing as a dominant cause of winter polynyas north of Greenland. We assert that dynamical downscaling using a high-resolution regional climate model offers a robust tool for process-level examination in space and time, synthesis with limited observations, and probabilistic forecasts of Arctic events, such as the ones being investigated here and elsewhere.

Re-examining flow pathways over the Chukchi Sea continental shelf

Velocity records from 8 mooring sites (2010–2019) and trajectories of drogued drifters (2012–2020) are used to evaluate patterns of flow on the Chukchi Sea continental shelf, with an emphasis on the latter three years (2016–2019). Together, these data provided insight into the temporal and spatial variability of the currents over this shelf. These data extend previous observations by five years and include three previously unoccupied sites, two of which span the Central Channel. Bathymetry directs a significant portion of the northward flow in Central Channel eastward across the Chukchi shelf where it joins the coastal flow prior to exiting Barrow Canyon and entering the Beaufort Sea. In addition, shelf-wide volume transport estimated from three mooring sites located off Icy Cape is modified from earlier analysis and extended in time. The resulting transport is highly correlated with that flowing through Central Channel, with similar magnitude. Icy Cape transport varies seasonally with variations in atmospheric forcing, as well as inter-annually, with an annual low of 0.24 Sv (September 2011–August 2012) to a annual high of 0.64 Sv (September 2017–August 2018), and a 9-year average of 0.43 Sv, or approximately 40% of the flow through Bering Strait. This nearly decade-long Icy Cape volume transport record also exhibits an increasing, although not significant, trend of ∼0.03 Sv/year.

Evaluation of Snow and Streamflows Using Noah-MP and WRF-Hydro Models in Aroostook River Basin, Maine

Snow influences land–atmosphere interactions in snow-dominated areas, and snow melt contributes to basin streamflows. However, estimating snowpack properties such as the snow depth (SD) and snow water equivalent (SWE) from land surface model simulations remains a challenge. This is, in part, due to uncertainties in the atmospheric forcing variables, which propagate into hydrological model predictions. This study implements the Weather Research and Forecasting (WRF)-Hydro framework with the Noah-Multiparameterization (Noah-MP) land surface model in the NOAA’s National Water Model (NWM) version 2.0 configuration to estimate snow in a single column and subsequently the streamflow across the Aroostook River’s sub-basins in Maine for water years (WY) 2014–2016. This study evaluates how differences between two atmospheric forcing datasets, the North American Land Data Assimilation version 2 (NLDAS-2) and in situ (Station), translate into differences in the simulation of snow. NLDAS-2 was used as the meteorological forcing in the retrospective NWM 2.0 simulations. The results from the single-column study showed that differences in the simulated SWE and SD were linked to differences in the 2 m air temperature (T2m), which influenced the precipitation partitioning of rain and snow, as parameterized in Noah-MP. The negative mean bias of −0.7 K (during the accumulation period) in T2m for NLDAS-2, compared to the Station forcing, was a major factor that contributed to the positive mean bias of +52 mm on average in the peak SWE in the NLDAS-2-forced Noah-MP simulation during the study period. The higher T2m values at the Station led to higher sensible heat fluxes towards the snowpack, which led to a higher amount of net energy at the snow’s surface and melt events during the accumulation season in Station-forced Noah-MP simulations. The results from the retrospective NWM version 2.0′s simulation in the basin showed that the streamflow estimates were closer to the United States Geological Survey gage observations at the two larger sub-basins (NSE = 0.9), which were mostly forested, compared to the two smaller sub-basins (NSE ≥ 0.4), which had more agricultural land-use. This study also showed that the spring snowmelt timing was captured quite well by the timing of the decline in the simulated SWE and SD, providing an early indication of melt in most sub-basins. The simulated fractional snow cover area (fSCA) however provided less information about the changes in snow or onset of snowmelt as it was mostly binary (full snow cover in winter), which differed from the more realistic fSCA values shown by the Moderate Resolution Imaging Spectroradiometer.

Observed Landscape Responsiveness to Climate Forcing

AbstractClimate variability and change shift environmental conditions on global land surfaces, creating uncertainties in predicting hydrologic flows, crop yields, and land carbon uptake. Land surfaces can present varying degrees of inertia to atmospheric forcing variability (e.g., precipitation). This study asks: are regions with the most variable environmental forcing necessarily the regions with the largest land surface variability? Specifically, it seeks to determine why land surfaces show varying responsiveness to environmental forcing. The degree to which and the mechanisms for how landscapes modulate the forcing are evaluated using a decade‐long satellite observation record of Africa's diverse climates. Surface responsiveness is quantified using intra‐seasonal energy flux variability, based on the observed diurnal temperature amplitude. We map the responsiveness and analyze the underlying mechanisms over intra‐seasonal timescales (especially interstorms). We show that, at a location, land surface responsiveness is dependent on the soil moisture distribution and the nonlinear relationship between energy fluxes and soil moisture. Land surfaces with greater responsiveness to climate are those with soil moisture distributions that span the threshold between evaporation regimes and spend most of their time in the water‐limited regime. Consequently, surface responsiveness mechanisms drive land surface variability beyond high climatic variability. Since we find these results to hold from intra‐seasonal to interannual timescales, we expect that these responsive regions will be most vulnerable to long‐term shifts in climate forcing. The quantification of these phenomena and determination of their geographic distributions based on observations can help assess land surface models used to evaluate hydrologic consequences of climate change.

Investigation of the Inherent Variability of the Mediterranean Sea Under Contrasting Extreme Climatic Conditions

The internal variability of the thermohaline circulation of the Mediterranean Sea is examined under contrasting extreme thermal and mass atmospheric forcing conditions. Three millennium-long numerical simulation experiments were performed under: (a) the current climatology, (b) a strong buoyancy forcing (SBF) scenario due to cold and dry conditions resembling the Younger Dryas event, and (c) a weak buoyancy forcing (WBF) scenario due to S1a sapropel deposition-like conditions (warm and wet). To isolate the inherent variability of the system, independent of interannual atmospheric forcing variability, the latter was defined as a perpetual year pertinent to each experiment. Self-diagnosed heat and salt fluxes, consistent to sea-surface characteristics of the above periods, forced three millenium-long, relaxation-free numerical experiments. These simulations were preceded by initial spin-up periods. The inherent spatiotemporal variability of the Mediterranean Sea was analyzed using the empirical orthogonal function (EOF) and spectral analysis on the simulated density fields. Our results revealed that the Mediterranean Sea exhibits high sensitivity to climatic conditions, allowing its circulation to change from anti-estuarine (for the SBF scenario, leading to a buoyancy loss to the atmosphere) to estuarine (for the WBF scenario, corresponding to a buoyancy gain from the atmosphere). In all three experiments, the interannual and decennial variabilities dominate in upper layers, and the decennial variability dominates in the Gibraltar and Sicily Straits. Under current climatic conditions the first two EOF modes express only 60% of the density variability in the deep layers. This contribution exceeds 90% under more extreme conditions. Moreover, the first EOF modes correspond to a basin-wide in-phase variability of the deep layers under the reference and WBF conditions. During SBF conditions the first modes reveal a vertical buoyancy exchange between upper and deeper layers. The second EOF mode of deep waters under both extreme scenarios showed that the western and eastern basins exchange buoyancy in decennial (for the cold/dry) and interdecennial (for the warm/humid) timescales. The residence time of the Eastern Mediterranean deep water was diagnosed to be centennial, semicentennial, and intercentennial for the cases of current period, SBF, and WBF, respectively.

Coastal windstorms create unsteady, unpredictable storm surges in a fluvial Maine estuary

Storm surges create coastal flooding that can be damaging to life and property. In estuaries with significant river influence (fluvial), it is possible for tides, storm surge, and river discharge to interact and enhance surges relative to the immediate coast. These tide-surge-river interactions were previously identified in a fluvial Maine estuary as higher frequency (>four cycles per day) oscillations to storm surge which were proposed to be incited by enhanced friction and resonance during certain windstorm events (Spicer et al. 2019). The relative contributions to tide-surge-river interaction from atmospheric forcing variables (wind, barometric pressure, and externally generated surge) remains unclear. This work seeks to decompose and analyze a recent windstorm surge event to better isolate the effects of atmospheric forcing on tidesurge- river interaction. Results show total storm surges in the fluvial estuary to be two times larger than at the estuary mouth because of tide-surge-river interaction. Analysis indicated at least 50% of the magnitude of tide-surge-river interactions are created by non-tidal forcing, in the form of wind, enhancing frictional energy in the estuary. The remaining tide-surge-river interaction is likely a result of changes in tidal wave propagation speed due to surge deepening the mean estuary water level.

Demons in the North Atlantic: Variability of Deep Ocean Ventilation

AbstractTranslation of atmospheric forcing variability into the ocean interior via ocean ventilation is an important aspect of transient climate change. On a seasonal timescale in the subtropics, this translation is mediated by a so‐called “Demon” that prevents access to all except late‐winter mixed‐layer water. Here, we use an eddy‐permitting numerical circulation model to investigate a similar process operating on longer (interannual) timescales in the subpolar North Atlantic. We find that variations in atmospheric forcing are mediated in their translation to the ocean interior, with year‐to‐year changes in the late‐winter mixed layer depth being the critical factor. The signature of persistent strong atmospheric forcing driving deep mixed layers is preferentially ventilated to the interior when the forcing is ceased. Susceptibility to this effect depends on the location and density of subduction—with the rate at which newly ventilated water escapes its region of subduction being the crucial factor.

Surface and Atmospheric Driven Variability of the Single‐Layer Urban Canopy Model Under Clear‐Sky Conditions Over London

AbstractUrban canopy models (UCMs) are parametrization schemes that are used to improve weather forecasts in urban areas. The performance of UCMs depends on understanding potential uncertainty sources that can generally originate from the (a) urban surface parameters, (b) atmospheric forcing, and (c) physical description. Here, we investigate the relative importance of surface and atmospheric driven model sensitivities of the single‐layer urban canopy model when fully interactive with a 1‐D configuration of the Weather Research and Forecasting model (WRF). The impact of different physical descriptions in UCMs and other key parameterization schemes of WRF is considered. As a case study, we use a 54‐hr period with clear‐sky conditions over London. Our analysis is focused on the surface radiation and energy flux partitioning and the intensity of turbulent mixing. The impact of changes in atmospheric forcing and surface parameter values on model performance appears to be comparable in magnitude. The advection of potential temperature, aerosol optical depth, exchange coefficient and roughness length for heat, surface albedo, and the anthropogenic heat flux are the most influential. Some atmospheric forcing variations have similar impact on the key physical processes as changes in surface parameters. Hence, error compensation may occur if one optimizes model performance using a single variable or combinations that have potential for carryover effects (e.g., temperature). Process diagrams help differences to be understood in the physical description of different UCMs, boundary layer, and radiation schemes and between the model and the observations.

Changing Occurrences of Fall Blooms Associated With Variations in Phytoplankton Size Structure in the Pacific Arctic

Phytoplankton blooms in the Pacific Arctic have been characterized as a huge single bloom in spring. However, several studies have reported recent increases in the occurrence of fall blooms during the period after dissipation of the spring bloom. Here, we explored spatiotemporal variations in the occurrence of fall blooms in the region, using satellite remote-sensing data for 2003–2017. Seasonal time-series variation in satellite-derived chlorophyll-a concentrations (chla) was modeled using a Gaussian function to distinguish whether a peak in chla was evident in fall; an occurrence of a fall bloom was recognized if the model detected the presence of a chla peak in fall. In addition, phytoplankton size structure was estimated from the satellite data to examine the influence of fall blooms on seasonal variations in the size structure. The results indicate that fall blooms occurred in a wide area of the Pacific Arctic, and larger phytoplankton were predominant during fall blooms relative to the phytoplankton present before/after the bloom or in the absence of a fall bloom. Examining interannual variation in occurrences of fall blooms revealed clear increasing and decreasing trends in the southern Chukchi Sea and the St. Lawrence Island polynya region, respectively, possibly associated with temporal variations in atmospheric forcing as well as in the water-column structure. Because the cell sizes of phytoplankton largely determine their sinking rate, temporal trends in the occurrence of fall blooms modulating seasonal variations in phytoplankton size structure could significantly influence marine ecosystems. These results suggest that spatiotemporal monitoring of phytoplankton communities not only in spring but also after the period or their dissipation would improve our understanding about processes causing variations within marine ecosystems, as might occur in the Pacific Arctic.

A 10-Yr Global Land Surface Reanalysis Interim Dataset (CRA-Interim/Land): Implementation and Preliminary Evaluation

A land surface reanalysis dataset covering the most recent decades is able to provide temporally consistent initial conditions for weather and climate models, and thus is crucial to verifying/improving numerical weather/climate forecasts/predictions. In this paper, we report the development of a 10-yr China Meteorological Administration (CMA) global Land surface ReAnalysis Interim dataset (CRA-Interim/Land; 2007–2016, 6-h intervals, approximately 34-km horizontal resolution). The dataset was produced and evaluated by using the Global Land Data Assimilation System (GLDAS) and NCEP Climate Forecast System Reanalysis (CFSR) global land surface reanalysis datasets, as well as in situ observations in China. The results show that the global spatial patterns and monthly variations of the CRA-Interim/Land, GLDAS, and CFSR climatology are highly consistent, while the soil moisture and temperature values of the CRA-Interim/Land dataset are in between those of the GLDAS and CFSR datasets. Compared with ground observations in China, CRA-Interim/Land soil moisture is comparable to or better than that of GLDAS and CFSR datasets for the 0–10-cm soil layer and has higher correlations and slightly lower root mean square errors (RMSE) for the 10–40-cm soil layer. However, CRA-Interim/Land shows negative biases in 10–40-cm soil moisture in Northeast China and north of central China. For ground temperature and the soil temperature in different layers, CRA-Interim/Land behaves better than the CFSR, especially in East and central China. CRA-Interim/Land has added value over the land components of CRA-Interim due to the introduction of global precipitation observations and improved soil/vegetation parameters. Therefore, this dataset is potentially a critical supplement to the CRA-Interim. Further evaluation of the CRA-Interim/Land, assimilation of near-surface atmospheric forcing variables, and extension of the current dataset to 40 yr (1979–2018) are in progress.

Contributions of Atmospheric Forcing and Chaotic Ocean Variability to Regional Sea Level Trends Over 1993–2015

AbstractA global ¼° ocean/sea‐ice 50‐member ensemble simulation is analyzed to disentangle the imprints of the atmospheric forcing and the chaotic ocean variability on regional sea level trends over the satellite altimetry period. We find that the chaotic ocean variability may mask atmospherically forced regional sea level trends over 38% of the global ocean area from 1993 to 2015, and over 47% of this area from 2005 to 2015. These regions are located in the western boundary currents, in the Southern Ocean and in the subtropical gyres. While these results do not question the anthropogenic origin of global mean sea level rise, they give new insights into the intrinsically oceanic versus atmospheric forcing of regional sea level trends and provide new constraints on the measurement time required to attribute regional sea level trends to the atmospheric forcing or to climate change.

Abrupt Intensification and Dissipation of Tropical Cyclones in Indian Ocean: A Case Study of Tropical Cyclone Nilofar – 2014

This study aims to investigate the possible influence of different atmospheric forcing on intensification/dissipation of tropical cyclonic “Nilofar” in Arabian Sea appeared during the last week of October, 2014 which exhibited abrupt intensification and dissipation as well. The cyclone was monitored by the Tropical Cyclone Warning Center (TCWC) of Pakistan Meteorological | department and the Regional Specialized Meteorological Center (RSMC) of Indian Meteorological | department (IMD) continuously, issued warnings and advisories with the help of available synoptic observations, satellite data and numerical models. Almost all the essential ingredients for intensification and tracking of the cyclone were studied and monitored accurately. Although the track forecast of the cyclone remained up to mark; but great errors occurred in intensity forecast. The atmospheric vertical wind shear could not be studied accurately. The intensity of wind shear itself is dependent on both; the local and global atmospheric forcing and climate variables, reoccurring periodically, especially while occurring two or more at the same time. More studies are required for influence of these climate variables while co-occurring at the same time period. This study will help weather forecasters to pay special attention on variation of climate factors affecting the wind shear for proper forecasting of tropical cyclones in the Arabian Sea for the safety of coastal communities along the coast.

Decadal variations and trends of the global ocean carbon sink

AbstractWe investigate the variations of the ocean CO2 sink during the past three decades using global surface ocean maps of the partial pressure of CO2 reconstructed from observations contained in the Surface Ocean CO2 Atlas Version 2. To create these maps, we used the neural network‐based data interpolation method of Landschützer et al. (2014) but extended the work in time from 1998 to 2011 to the period from 1982 through 2011. Our results suggest strong decadal variations in the global ocean carbon sink around a long‐term increase that corresponds roughly to that expected from the rise in atmospheric CO2. The sink is estimated to have weakened during the 1990s toward a minimum uptake of only −0.8 ± 0.5 Pg C yr−1 in 2000 and thereafter to have strengthened considerably to rates of more than −2.0 ± 0.5 Pg C yr−1. These decadal variations originate mostly from the extratropical oceans, while the tropical regions contribute primarily to interannual variations. Changes in sea surface temperature affecting the solubility of CO2 explain part of these variations, particularly at subtropical latitudes. But most of the higher‐latitude changes are attributed to modifications in the surface concentration of dissolved inorganic carbon and alkalinity, induced by decadal variations in atmospheric forcing, with patterns that are reminiscent of those of the Northern and Southern Annular Modes. These decadal variations lead to a substantially smaller cumulative anthropogenic CO2 uptake of the ocean over the 1982 through 2011 period (reduction of 7.5 ± 5.5 Pg C) relative to that derived by the Global Carbon Budget.

Forcing variables in simulation of transpiration of water stressed plants determined by principal component analysis

AbstractTo date, measuring plant transpiration at canopy scale is laborious and its estimation by numerical modelling can be used to assess high time frequency data. When using the model by Jacobs (1994) to simulate transpiration of water stressed plants it needs to be reparametrized. We compare the importance of model variables affecting simulated transpiration of water stressed plants. A systematic literature review was performed to recover existing parameterizations to be tested in the model. Data from a field experiment with common bean under full and deficit irrigation were used to correlate estimations to forcing variables applying principal component analysis. New parameterizations resulted in a moderate reduction of prediction errors and in an increase in model performance. Agsmodel was sensitive to changes in the mesophyll conductance and leaf angle distribution parameterizations, allowing model improvement. Simulated transpiration could be separated in temporal components. Daily, afternoon depression and long-term components for the fully irrigated treatment were more related to atmospheric forcing variables (specific humidity deficit between stomata and air, relative air humidity and canopy temperature). Daily and afternoon depression components for the deficit-irrigated treatment were related to both atmospheric and soil dryness, and long-term component was related to soil dryness.

Estimates of Global Surface Hydrology and Heat Fluxes from the Community Land Model (CLM4.5) with Four Atmospheric Forcing Datasets

Abstract Global land surface hydrology and heat fluxes can be estimated by running a land surface model (LSM) driven by the atmospheric forcing dataset. Previous multimodel studies focused on the impact of different LSMs on model results. Here the sensitivity of the Community Land Model, version 4.5 (CLM4.5), results to the atmospheric forcing dataset is documented. Together with the model default global forcing dataset (CRU–NCEP, hereafter CRUNCEP), three newly developed, reanalysis-based, near-surface meteorological datasets (i.e., MERRA, CFSR, and ERA-Interim) with the precipitation adjusted by the Global Precipitation Climatology Project monthly product were used to drive CLM4.5. All four simulations were run at 0.5° × 0.5° grids from 1979 to 2009 with the identical initialization. The simulated monthly surface hydrology variables, fluxes, and the forcing datasets were then evaluated against various observation-based datasets (soil moisture, runoff, snow depth and water equivalent, and flux tower measurements). To partially avoid the mismatch between model gridbox values and point measurements, three approaches were taken. The model simulations based on three newly constructed forcing datasets are overall better than the simulation from CRUNCEP, in particular for soil moisture and snow quantities. The ensemble mean from the CLM4.5 simulations using the four forcing datasets is generally superior to individual simulations, and the ensemble mean latent and sensible heat fluxes over global land (60°S–90°N) are 42.8 and 40.3 W m−2, respectively. The differences in both precipitation and other atmospheric forcing variables (e.g., air temperature and downward solar radiation) contribute to the differences in simulated results. The datasets are available from the authors for further evaluation and for various applications.

Atmospheric forcing of the upper ocean transport in the Gulf of Mexico: From seasonal to diurnal scales

AbstractThe 2010 Deepwater Horizon oil spill in the Gulf of Mexico (GoM) was an environmental disaster, which highlighted the urgent need to predict the transport and dispersion of hydrocarbon. Although the variability of the atmospheric forcing plays a major role in the upper ocean circulation and transport of the pollutants, the air‐sea interaction on various time scales is not well understood. This study provides a comprehensive overview of the atmospheric forcing and upper ocean response in the GoM from seasonal to diurnal time scales, using climatologies derived from long‐term observations, in situ observations from two field campaigns, and a coupled model. The atmospheric forcing in the GoM is characterized by striking seasonality. In the summer, the time‐average large‐scale forcing is weak, despite occasional extreme winds associated with hurricanes. In the winter, the atmospheric forcing is much stronger, and dominated by synoptic variability on time scales of 3–7 days associated with winter storms and cold air outbreaks. The diurnal cycle is more pronounced during the summer, when sea breeze circulations affect the coastal regions and nighttime wind maxima occur over the offshore waters. Realtime predictions from a high‐resolution atmosphere‐wave‐ocean coupled model were evaluated for both summer and winter conditions during the Grand LAgrangian Deployment (GLAD) in July–August 2012 and the Surfzone Coastal Oil Pathways Experiment (SCOPE) in November–December 2013. The model generally captured the variability of atmospheric forcing on all scales, but suffered from some systematic errors.