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At least 181 records · Page 10

Observational Needs for Improving Ocean and Coupled Reanalysis, S2S Prediction, and Decadal Prediction

Developments in ocean data assimilation (DA) and observing system technologies are intertwined. New observation types lead to new DA methods, and new DA methods such as Coupled Data Assimilation can change the value of existing observations or indicate where new observations can have greater utility for monitoring and prediction. Practitioners are encouraged to make better use of observations that are already available, for example in strongly coupled data assimilation where ocean observations can be used to improve atmospheric analyses and vice versa. Ocean reanalyses are useful for the analysis of climate,as well as initializing operational long-range prediction models. There are remaining challenges for ocean reanalyses due to biases and abrupt changes in the ocean observing system throughout its history, the presence of biases and drifts in models, and simplifying assumptions made in the DA methods. From a governance point of view, more support is needed to interface the observing community and the ocean DA community. For prediction applications, the ocean DA community must work with the ocean observing community to establish protocols for rapid communication of ocean observing data on NWP timescales. There is potential for new observations to enhance the observing system by supporting prediction on multiple timescales, ranging from the typical timescale of numerical weather prediction covering hours to weeks, out to multiple decades. It is highly encouraged that communication be fostered between thesecommunities to allow operational prediction centers the ability to provide guidance to the design of a sustained and adaptive observing network.

Ocean reanalysis↗

Utilization of Airborne Observations to Assess Model Parametrizations of Critical RH Profiles in the Arctic Ocean

Observational paucity is a reality in the Arctic Ocean. This is especially true for near-surface variables such as temperature, moisture, heat fluxes and BL clouds. As a result, modeling has become one of the major avenues for understanding current and future Arctic trends. Reanalyses are frequently used to force global ocean circulation and sea-ice models. But in northern high latitudes, model integrations and reanalyses are known to have large uncertainties in temperature and humidity profiles, and in boundary layer cloudiness. These are common sources of error in the surface radiative budget terms.An important way to diagnose these biases spatially and temporally is by using satellite remote sensing data. However, remotely-sensed observations also have large uncertainties, especially in near-surface temperature and relative humidity profiles.In situ observational studies are important in bridging our knowledge gap in regions such as the Arctic Ocean. Here, we utilize airborne and ship observations from the ARISE, ACME-IV, and ASCOS campaigns to construct critical relative humidity (RH) profiles over the Beaufort Sea. Such profiles are used as parameterization inputs in the NASA GOES global model to derive the total water condensate in a model grid-box, which determines the cloud fraction. Currently, the critical RH profiles are derived by global AIRS data, relaying mostly on mid-latitude regions, which are not necessarily relevant to the Arctic.We derive campaign-wide mean, standard deviation, and critical RH values, for grid size of 50x50 km and altitude bins between 50 to 400 m, covering both open-ocean and sea-ice covered regions. We compare profiles over open ocean and sea-ice, and look at correlations between the observed critical RH values and water condensate (by cloud number concentration) from observations versus the modeled ones. We then input our calculated values of minimal critical RH values into a set of GEOS single column model (SCM) simulations over the ARISE and ASCOS regions and compare the differences between the predicted values of cloud liquid water path (LWP), ice water path (IWP) and surface fluxes with the observed ones under the range of input parameterizations. Finally, we discuss the implications on surface radiative budget predictions in this region.

Segal-Rozenhaimer, Michal↗

A Moments View of Climatology and Variability of the Asian Summer Monsoon Anticyclone

A comprehensive investigation of the climatology of and interannual variability and trends in the Asian summer monsoon anticyclone (ASMA) is presented, based on a novel area and moments analysis. Moments include centroid location, aspect ratio, angle, and “excess kurtosis” (measuring how far the shape is from elliptical) for an equivalent ellipse with the same area as the ASMA. Key results are robust among the three modern reanalyses studied. The climatological ASMA is nearly elliptical, with its major axis aligned along its centroid latitude and a typical aspect ratio of ∼5–8. The ASMA centroid shifts northward with height, northward and westward during development, and in the opposite direction as it weakens. New evidence finding no obvious climatological bimodality in the ASMA reinforces similar suggestions from previous studies using modern reanalyses. Most trends in ASMA moments are not statistically significant. ASMA area and duration, however, increased significantly during 1979–2018; the 1958–2018 record analyzed for one reanalysis suggests that these trends may have accelerated in recent decades. ASMA centroid latitude is significantly positively (negatively) correlated with subtropical jet core latitude (altitude), and significantly negatively correlated with concurrent ENSO; these results are consistent with and extend previous work relating monsoon intensity, ENSO, and jet shifts. ASMA area is significantly positively correlated with the MEI ENSO index two months previously. These results improve our understanding of the ASMA using consistently defined diagnostics of its size, geometry, interannual variability, and trends that have not previously been analyzed.

Anticyclone↗

Variations in radiative heating of humid biomass burning aerosols in the southeast Atlantic from airborne observations and reanalysis

The atmosphere over the southeast Atlantic Ocean (SEA) experiences consistent springtime biomass burning (BB) smoke from widespread agricultural fires on the African continent. This smoke layer is initially lofted high in a continental mixed layer (~5-6km) and is then transported westward into the free troposphere where it overlies and ultimately mixes into a stratocumulus-topped oceanic boundary layer. Coincident with this smoke is an elevated humidity signal which is present from the time a given airmass is over the continental source region. ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) was a NASA Earth Venture Suborbital mission with the goal of measuring aerosol, cloud, and atmospheric properties over this region over three deployments in September 2016, August 2017, and October 2018. Measurements included in situ profile measurements of aerosol physical and optical properties, trace gas concentrations, and cloud properties, as well as column measurements of aerosol optical depth and trace gas concentrations from an airborne sun photometer. Here we present results from each of the ORACLES deployments over the southeast Atlantic Ocean. Despite the differing locations and season of each deployment, we show that there is good agreement between the airborne ORACLES dataset and large-scale reanalyses, specifically the ECMWF ERA5 reanalyses (and, to a degree, the CAMS product) as compared to other available products. This agreement allows us to examine multi-year seasonal patterns and trends beyond the three months with available aircraft data. In both the data and reanalysis, we find distinct variations between each deployment in terms of vertical smoke distribution and correlation to atmospheric specific humidity, due to changing conditions over the BB season. We trace the origin and the history of these smoky airmasses and finally, we briefly discuss the broader radiative and dynamical implications of these results for conditions of aerosols overlying stratocumulus clouds.

K. Pistone↗

Effect of Rain-Adjusted Satellite Sea Surface Salinity on ENSO Predictions from the GMAO S2S Forecast System

ENSO has a significant impact on climate variability throughout the world and so has been the key focus for improving coupled ocean-atmosphere forecasts. Assimilation of altimetry and in situ data lead to improved short-term forecasts of the coupled system. However, few studies have focused on improving the near-surface density/mixing through assimilation of satellite sea surface salinity (SSS). For expediency, most projects that do assimilate SSS do so as if these data were observed at the top model layer instead of at the surface. In rainy regions where buoyant water sits as a fresh lens at the surface, this assumption is likely invalid. Therefore, we adjust SSS so that it more accurately represents the salinity at the first model layer. The Rain Impact Model (RIM) uses a simple diffusion model to determine the near surface salinity gradient (i.e., 1 cm to 5 m). Satellite SSS data are modified using this near-surface salinity gradient, so the salinity values are now valid at the first model layer (we call this SSS@5m). We assess the impact of satellite SSS observations for near-surface dynamics within ocean reanalyses and how these impact dynamical ENSO forecasts using the NASA GMAO S2S coupled system. For all reanalysis experiments, all available along-track sea level and in situ observations are assimilated for 2011 to 2020 using the LETKF scheme (Penny et al., 2013). One reanalysis assimilates Aquarius /SMAP SSS as before. An additional reanalysis is performed assimilating the SSS@5m data. Validation statistics are compared for experiments that assimilate SSS (sub-optimally as before) versus the SSS@5m. We also compare results of coupled forecasts that are initialized from these reanalyses in spring. For all but the big La Niña in 2017, all NINO3.4 forecasts were improved by using the RIM. We will show that improved SSS estimates upgrades density and near-surface mixing leading to more accurate coupled air/sea interaction and better forecasts.

ENSO↗

PolarMERRA: A Polar-Focused Global Reanalysis Project for Scientific and Stakeholder Needs

The adequate modeling of physical processes in the Arctic and Antarctic is vital for developing prediction capabilities and for obtaining an understanding of rapidly evolving polar conditions, including their potential global impacts. These processes are often poorly represented in global models and reanalyses, owing to a legacy modeling focus on midlatitude processes, as well as a scarcity of observations needed for model development in polar regions. The polarMERRA initiative, a joint effort between NASA’s Cryospheric Sciences and Modeling and Prediction programs, seeks to improve the representation of cryospheric and polar atmospheric processes and to develop an open-source framework for a quantitively evaluation of polar-relevant variables against current and future satellite and in-situ observations, models, and reanalyses. Here, we evaluate the impacts of spatial resolution and modifications to sea ice, ice sheet, and atmospheric parameterizations on the representation of high latitude conditions by using a quantitative scorecard approach that leverages NASA’s extensive satellite record. Investigations are conducted using the NASA Goddard Earth Observing System model (GEOS) and its data assimilation system (GEOS DAS). Through a quantitative identification of process deficiencies, bias reductions in key surface variables, including temperature and precipitation over cryospheric surfaces, may be achieved. The polarMERRA project additionally seeks to identify additional data sources for use in the GEOS DAS, and to incorporate data and parameterization improvements into future model and reanalysis products for scientific and stakeholder use.

Lauren C Andrews↗

Regional Impacts of Ice Sheet Surface Representation Improvements in an Earth System Model

Ice sheet surface conditions and their subsequent impact on surface mass balance play an important role in ice sheet dynamics and ice sheet interaction with the overlying atmosphere and surrounding ocean. The NASA Global Modeling and Assimilation Office’s (GMAO) Goddard Earth Observing System (GEOS) ecosystem of models and reanalyses model ice sheet and glacier surface mass balance with a moderately complex snow and ice module, including prognostic surface albedo evolution, fractional snow cover, and snowpack hydrology and meltwater retention. With this configuration, Greenland near surface air temperatures and ice sheet melt are well represented in MERRA-2, GMAO’s current atmospheric reanalysis; however, the similarity to observations in some regions is due, in part, to compensating biases in surface energy budget. Recent improvements to GEOS’ cloud microphysics and longwave radiation parameterizations reduced biases in surface net longwave radiation and exacerbated surface net shortwave radiation biases, effectively increasing near surface temperatures and melting, particularly during the summer months. Here, we demonstrate the Earth system response to an improved representation of the ice sheet surface focused on minimizing the surface energy biases and increasing spatial and temporal representativeness. New changes to the surface albedo parameterization, including the introduction of a prognostic aerosol darkening scheme, help mitigate shortwave biases, while modifications to surface roughness characteristics improve near surface sensible heat fluxes. Focusing on the Arctic and Greenland, we examine the combined impact on these changes on regional weather conditions and ice sheet surface mass balance using a high resolution atmospheric only experiment, and we use a coupled model configuration to assess the impact on oceanic conditions and subseasonal-to-seasonal prediction. Overall, these improvements increase modeled ice sheet surface realism and provide a strong basis for more accurate ice sheet mass balance in GMAO’s future reanalyses and forecasting systems.

Lauren C Andrews↗

Regional Impacts of Ice Sheet Surface Representation Improvements in an Earth System Model

Ice sheet surface conditions and their subsequent impact on surface mass balance play an important role in ice sheet dynamics and ice sheet interaction with the overlying atmosphere and surrounding ocean. The NASA Global Modeling and Assimilation Office’s (GMAO) Goddard Earth Observing System (GEOS) ecosystem of models and reanalyses model ice sheet and glacier surface mass balance with a moderately complex snow and ice module, including prognostic surface albedo evolution, fractional snow cover, and snowpack hydrology and meltwater retention. With this configuration, Greenland near surface air temperatures and ice sheet melt are well represented in MERRA-2, GMAO’s current atmospheric reanalysis; however, the similarity to observations in some regions is due, in part, to compensating biases in surface energy budget. Recent improvements to GEOS’ cloud microphysics and longwave radiation parameterizations reduced biases in surface net longwave radiation and exacerbated surface net shortwave radiation biases, effectively increasing near surface temperatures and melting, particularly during the summer months. Here, we demonstrate the Earth system response to an improved representation of the ice sheet surface focused on minimizing the surface energy biases and increasing spatial and temporal representativeness. New changes to the surface albedo parameterization, including the introduction of a prognostic aerosol darkening scheme, help mitigate shortwave biases, while modifications to surface roughness characteristics improve near surface sensible heat fluxes. Focusing on the Arctic and Greenland, we examine the combined impact on these changes on regional weather conditions and ice sheet surface mass balance using a high resolution atmospheric only experiment, and we use a coupled model configuration to assess the impact on oceanic conditions and subseasonal-to-seasonal prediction. Overall, these improvements increase modeled ice sheet surface realism and provide a strong basis for more accurate ice sheet mass balance in GMAO’s future reanalyses and forecasting systems.

Lauren C. Andrews↗

Predictability of the early summer surface air temperature over Western South Asia

Variability of the Surface Air Temperature (SAT) over the Western South Asia (WSA) region leads to frequent heatwaves during the early summer (May-June) season. The present study uses the European Centre for Medium-Range Weather Forecast’s fifth-generation seasonal prediction system, SEAS5, from 1981 to 2022 based on April initial conditions (1-month lead) to assess the SAT predictability during early summer season. The goal is to evaluate the SEAS5’s ability to predict the El Niño-Southern Oscillation (ENSO) related interannual variability and predictability of the SAT over WSA, which is mediated through upper-level (200-hPa) geopotential height anomalies. This teleconnection leads to anomalously warm surface conditions over the region during the negative ENSO phase, as observed in the reanalysis and SEAS5. We evaluate SEAS5 prediction skill against two observations and three reanalyses datasets. The SEAS5 SAT prediction skill is higher with high spatial resolution observations and reanalysis datasets compared to the ones with low-resolution. Overall, SEAS5 shows reasonable skill in predicting SAT and its variability over the WSA region. Moreover, the predictability of SAT during La Niña is comparable to El Niño years over the WSA region.

54 ENVIRONMENTAL SCIENCES↗

Closure of Earth’s Global Seasonal Cycle of Energy Storage

Abstract The global seasonal cycle of energy in Earth’s climate system is quantified using observations and reanalyses. After removing long-term trends, net energy entering and exiting the climate system at the top of the atmosphere (TOA) should agree with the sum of energy entering and exiting the ocean, atmosphere, land, and ice over the course of an average year. Achieving such a balanced budget with observations has been challenging. Disagreements have been attributed previously to sparse observations in the high-latitude oceans. However, limiting the local vertical integration of new global ocean heat content estimates to the depth to which seasonal heat energy is stored, rather than integrating to 2000 m everywhere as done previously, allows closure of the global seasonal energy budget within statistical uncertainties. The seasonal cycle of energy storage is largest in the ocean, peaking in April because ocean area is largest in the Southern Hemisphere and the ocean’s thermal inertia causes a lag with respect to the austral summer solstice. Seasonal cycles in energy storage in the atmosphere and land are smaller, but peak in July and September, respectively, because there is more land in the Northern Hemisphere, and the land has more thermal inertia than the atmosphere. Global seasonal energy storage by ice is small, so the atmosphere and land partially offset ocean energy storage in the global integral, with their sum matching time-integrated net global TOA energy fluxes over the seasonal cycle within uncertainties, and both peaking in April.

54 ENVIRONMENTAL SCIENCES↗

On the relationship between eastern China aerosols and western North Pacific tropical cyclone activity

How aerosols affect the weather and climate system has received increasing attention. This study finds a significant negative correlation between March–May eastern China aerosol optical depth (AOD) and July–November western North Pacific (WNP) tropical cyclone (TC) frequency during 2003–2020. This time period spans when several aerosol reanalyses are available and both Terra and Aqua Moderate Resolution Imaging Spectroradiometer AOD retrievals are assimilated therein. Composite analyses and budget analyses of dynamical genesis potential indices indicate the importance of large-scale environmental factors, especially vertical velocity and vertical wind shear, associated with changes in AOD that in turn modulate changes in WNP TC frequency. Increased eastern China AOD may facilitate negative Pacific meridional mode development via modulation of the westerly jet, which then forces an anticyclonic circulation over the WNP basin. Increased AOD can also directly decrease the inter-hemispheric temperature differential and increases the intra-hemispheric temperature differential between the equator and the mid-latitudes, thus weakening ascending motion and enhancing vertical wind shear over the WNP, especially the southeastern portion of the basin. Further, all of these large-scale environment changes induced by increased eastern China AOD tend to suppress WNP TCs. This study highlights the potential influence of eastern China aerosol loadings on WNP TCs, thus improving our understanding of TC climate variability over the WNP.

54 ENVIRONMENTAL SCIENCES↗

A reporting format for field measurements of soil respiration

Field observations of the soil-to-atmosphere CO2 flux–soil respiration, RS–are a prime example of ‘long tail’ data that historically have had neither centralized databases nor an agreed-upon reporting format. This has hindered scientific transparency, analytical reproducibility, and novel syntheses with respect to this globally-important component of the carbon cycle. Here we propose a new data and metadata reporting format for RS data, based on engagement with a wide range of researchers in the field as well as expert advisory panels. Our goal was a reporting format that would be relevant and useful for synthesis activities, and optimizing data discoverability and usability while not placing an undue burden on data contributors. We describe previous RS data collection efforts, lessons learned from related databases and data-oriented networks (e.g. FLUXNET) in earth and ecological sciences, and the process of community consultation. The proposed reporting format focuses on chamber-level data and metadata, specifying measurement conditions and, for a given measurement period defined by beginning and ending timestamps, a mean RS flux (or CO2 concentration) and associated ancillary measurements. Fundamentally, this format aims to enable findable, accessible, interoperable, and reusable data, while providing ‘future-proofing’ capabilities to support reanalyses using as yet unknown algorithms or approaches. Finally, this proposed RS reporting format is available online, and is intended to be a dynamic document, subject to further community feedback and/or change in the future.

Bond-Lamberty, Benjamin↗

No evidence of a reduction in the dipole polarisability at shell closures

Photoabsorption reactions have a long and productive history as probes of the response of nuclei to electromagnetic fields. In recent years, there has been a suggestion that the dipole polarisability may be reduced at shell closures, potentially due to the reduced level density. In the current paper, more recent photoabsorption strengths are reanalysed including nuclei on closed proton shells to search for a similar reduction. The previously reported reduction is not reproduced. We conclude that the previously observed reduction may be due to incomplete evaluations of the photoabsorption due to missing cross section contributions and experimental artefacts. More experimental data are necessary to draw a firm conclusion about whether shell effects persist into high excitation energies, especially data which can obviate any systematic effects and look at relative photoabsorption strengths between nuclei.

Giant dipole response↗

Assessing the Representation of Synoptic Variability Associated With California Extreme Precipitation in CMIP6 Models

Abstract Days of extreme precipitation over California are evaluated in Coupled Model Intercomparison Project Phase 6 (CMIP6) models and the ERA‐Interim reanalysis. In the current climate, the model spread in composited precipitation on extreme precipitation days is closely related to the magnitude of composited integrated vapor transport (IVT) across models, a proxy for the intensity of atmospheric rivers. Most models underestimate the magnitude of IVT associated with extreme precipitation, according to ERA‐Interim. This is due mostly to the contribution of moisture, which almost all models overestimate, while the contribution of lower‐tropospheric wind speed is generally closer to the reanalyses. Moreover, most models underestimate the variance in the latitude of maxima of numerous variables among days of extreme California precipitation. That is, in the general circulation models there is a lack of diversity in the latitude of the disturbances bringing winter precipitation to California. In the future climate, most models project a decrease in the frequency of southward‐displaced disturbances among California extreme precipitation days. Hence, the greatest increases in extreme precipitation are over northern California. However, the historical underestimate of the latitudinal variance of disturbances calls into question the reliability of these projections. This bias should be especially considered for dynamical downscaling efforts over the region.

Norris, Jesse↗

Observations Indicate That Clouds Amplify Mechanisms of Southern Ocean Heat Uptake

Abstract The Southern Ocean has absorbed most of the excess heat associated with anthropogenic greenhouse gas emissions. Since Southern Ocean observations are sparse in certain regions and seasons, much of our knowledge of ocean heat uptake is based on climate model simulations. However, climate models still inadequately represent some properties of Southern Ocean clouds, and they have not identified the mechanisms by which clouds may affect Southern Ocean heat uptake (SOHU). Here, we use the ERA5 and JRA‐55 reanalyses to assess the influence of clouds and other atmospheric processes on SOHU from 1979 to 2020. We find that years with the highest SOHU between 45° and 65°S are dominated by ocean heat uptake anomalies during winter and spring, but not during summer or fall. Winter and spring cloud cover are up to 7% higher when SOHU is up to 5.5 W/m 2 higher than the climatological seasonal mean, with the largest increases in the South Pacific Ocean. Clouds also contain more liquid water. These changes in cloud properties increase downwelling longwave radiation, amplifying ocean heat uptake. Cloud changes are also concomitant with a more stable lower atmosphere, which suppresses turbulent heat fluxes out of the surface. Overall, we find that SOHU is likely not mediated by enhanced surface shortwave absorption over the observational time period. A better understanding of how atmospheric processes impact ocean heat uptake may help improve our understanding of ocean heat uptake mechanisms in the current generation of climate models.

54 ENVIRONMENTAL SCIENCES↗

Connecting Large‐Scale Meteorological Patterns to Extratropical Cyclones in CMIP6 Climate Models Using Self‐Organizing Maps

Abstract Extratropical cyclones (ETCs) are responsible for the majority of cool‐season extreme events in the northeastern United States (NEUS), often leading to high‐impact weather conditions that can have wide‐ranging socioeconomic impacts. Evaluating the ability of climate models to adequately simulate ETC dynamics is essential for improving model performance and increasing confidence in future projections used by stakeholders and policymakers. ETCs are traditionally studied using techniques such as case studies and synoptic typing, however, these approaches can be time‐consuming, require subjective analysis, and do not necessarily identify the coincident large‐scale meteorological patterns (LSMPs). Here, we apply self‐organizing maps (SOMs) as an automated machine‐learning approach to characterize the LSMPs and associated frequency and intensity of discrete ETC events over NEUS. The dominant patterns of geopotential height variability are identified through SOM analysis of five reanalysis products during the last four decades. ETC events are tracked using TempestExtremes and are integrated with SOMs to classify the accumulated cyclone activity (ACA) associated with each pattern. We then evaluate the skill of CMIP6 historical experiments in simulating the LSMPs and ETC events identified in the SOM. Our results identify a robust bias toward more zonal patterns, with models struggling to reproduce the more amplified patterns typically associated with the highest cyclone activity. While model resolution has some impact on simulation credibility, model configuration appears to be more important in LSMP representation. The vast majority of CMIP6 models produce too few ETCs, although model errors are distributed around historical reanalyses when ACA is normalized by storm frequency.

54 ENVIRONMENTAL SCIENCES↗

Evaluating Uncertainty and Modes of Variability for Antarctic Atmospheric Rivers

Abstract Antarctic atmospheric rivers (ARs) are driven by their synoptic environments and lead to profound and varying impacts along the coastlines and over the continent. The definition and detection of ARs over Antarctica accounts for large uncertainty in AR metrics, and consequently, impacts quantification. We find that Antarctic‐specific detection tools consistently capture the AR footprint inland over ice sheets, whereas most global detection tools do not. Large‐scale synoptic environments and associated ARs, however, are broadly consistent across detection tools. Using data from the Atmospheric River Tracking Method Intercomparison Project and global reanalyses, we quantify the uncertainty in Antarctic AR metrics and evaluate large‐scale environments in the context of decadal and interannual modes of variability. The Antarctic western hemisphere has stronger connections to both decadal and interannual modes of variability compared to East Antarctica, and the Indian Ocean Dipole’s influence on Antarctic ARs is stronger while in phase with El Nino Southern Oscillation.

54 ENVIRONMENTAL SCIENCES↗

Contribution of Atmospheric Rivers to Antarctic Precipitation

Abstract Atmospheric rivers (ARs) are efficient mechanisms for transporting atmospheric moisture from low latitudes to the Antarctic Ice Sheet (AIS). While AR events occur infrequently, they can lead to extreme precipitation and surface melt events on the AIS. Here we estimate the contribution of ARs to total Antarctic precipitation, by combining precipitation from atmospheric reanalyses and a polar‐specific AR detection algorithm. We show that ARs contribute substantially to Antarctic precipitation, especially in East Antarctica at elevations below 3,000 m. ARs contribute substantially to year‐to‐year variability in Antarctic precipitation. Our results highlight that ARs are an important component for understanding present and future Antarctic mass balance trends and variability.

54 ENVIRONMENTAL SCIENCES↗