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At least 19 records

Stochastic modeling of the time-averaged equations for climate dynamics

Two analyses on climate dynamics are presented, both based on a simplified set of barotropic equations for representing large scale non-linear atmospheric circulation characteristics. The statistical properties of the set of equations were investigated for small values of a parameter k sub o, corresponding to the physical case of large zonal westerlies and relatively weak eddy motions. The effect of seasonal type forcing on the solution of these equations was also studied and some preliminary numerical results are presented.

Spreiter, J. R.↗

Moving beyond post hoc explainable artificial intelligence: a perspective paper on lessons learned from dynamical climate modeling

AI models are criticized as being black boxes, potentially subjecting climate science to greater uncertainty. Explainable artificial intelligence (XAI) has been proposed to probe AI models and increase trust. In this review and perspective paper, we suggest that, in addition to using XAI methods, AI researchers in climate science can learn from past successes in the development of physics-based dynamical climate models. Dynamical models are complex but have gained trust because their successes and failures can sometimes be attributed to specific components or sub-models, such as when model bias is explained by pointing to a particular parameterization. We propose three types of understanding as a basis to evaluate trust in dynamical and AI models alike: (1) instrumental understanding, which is obtained when a model has passed a functional test; (2) statistical understanding, obtained when researchers can make sense of the modeling results using statistical techniques to identify input–output relationships; and (3) component-level understanding, which refers to modelers' ability to point to specific model components or parts in the model architecture as the culprit for erratic model behaviors or as the crucial reason why the model functions well. We demonstrate how component-level understanding has been sought and achieved via climate model intercomparison projects over the past several decades. Such component-level understanding routinely leads to model improvements and may also serve as a template for thinking about AI-driven climate science. Currently, XAI methods can help explain the behaviors of AI models by focusing on the mapping between input and output, thereby increasing the statistical understanding of AI models. Yet, to further increase our understanding of AI models, we will have to build AI models that have interpretable components amenable to component-level understanding. We give recent examples from the AI climate science literature to highlight some recent, albeit limited, successes in achieving component-level understanding and thereby explaining model behavior. The merit of such interpretable AI models is that they serve as a stronger basis for trust in climate modeling and, by extension, downstream uses of climate model data.

54 ENVIRONMENTAL SCIENCES↗

Mars: A Planet with a Dynamic Climate System

Mars is a well-observed planet. Since the 1960s orbiters, landers, rovers, and earth-based telescopic observations show that its climate system is dynamic. Its dynamic nature, largely the result of atmosphere-surface interactions, is most obvious in the seasonal cycles of dust, water, and carbon dioxide that define the planet's climate system. These cycles are linked through the global circulation and MGS, Odyssey, Phoenix, MER, Mars Express, MRO, and now MSL have continuously observed them at Mars for the past 16 years. Their observations show that while the seasonal cycles are largely annually repeatable, there are interannual variations. Planet-encircling dust storms, for example, are quasi-triennial and originate over a broader range of seasons and locations than previously thought. Water moves from pole-to-pole each year in a largely, but not precisely, repeatable pattern that suggests but does not demand non-polar surface reservoirs. And the seasonal CO2 polar caps grow and retreat in a very predictable way with only minor deviations from year-to-year in spite of significant differences in atmospheric dust content. These behaviors suggest a complicated but robust coupled system in which these cycles interact to produce the greatest interannual variability in the dust cycle and least variability in the CO2 cycle. The nature of these interactions is the subject of ongoing research, but clouds, both water ice and CO2 ice, now appear to play a bigger role than believed at the end of the 20th century. There may also be some long-term trends in these cycles as there is evidence from imaging data, for example, that the south polar residual cap may not be stable on decadal to centennial time scales. On even longer time scales, the discovery of as much as 5 mb global equivalent of buried CO2 ice near the south pole, the detection of vast quantities of subsurface water ice at very shallow depths in midlatitudes of both hemispheres, and the presence of remnant glacial features at almost all latitudes, strongly suggests the possibility of significant climate change associated with orbital variations. Some of the major questions these data raise concern how closed the seasonal cycles are and which reservoirs are gaining or loosing, the cause of the large interannual variability of the dust cycle and how it couples to the water and CO2 cycles, and the mechanisms for the origin of past glacial activity and the emplacement and removal of subsurface ice. While many of these questions can be addressed with continued research based on existing data, new observations focused on atmosphere surface-interactions would provide valuable constraints on how dust, water, and CO2 move between the surface and atmosphere.

CO2 cycles↗

Sensitivity properties of a biosphere model based on BATS and a statistical-dynamical climate model

A biosphere model based on the Biosphere-Atmosphere Transfer Scheme (BATS) and the Saltzman-Vernekar (SV) statistical-dynamical climate model is developed. Some equations of BATS are adopted either intact or with modifications, some are conceptually modified, and still others are replaced with equations of the SV model. The model is designed so that it can be run independently as long as the parameters related to the physiology and physiognomy of the vegetation, the atmospheric conditions, solar radiation, and soil conditions are given. With this stand-alone biosphere model, a series of sensitivity investigations, particularly the model sensitivity to fractional area of vegetation cover, soil surface water availability, and solar radiation for different types of vegetation, were conducted as a first step. These numerical experiments indicate that the presence of a vegetation cover greatly enhances the exchanges of momentum, water vapor, and energy between the atmosphere and the surface of the earth. An interesting result is that a dense and thick vegetation cover tends to serve as an environment conditioner or, more specifically, a thermostat and a humidistat, since the soil surface temperature, foliage temperature, and temperature and vapor pressure of air within the foliage are practically insensitive to variation of soil surface water availability and even solar radiation within a wide range. An attempt is also made to simulate the gradual deterioration of environment accompanying gradual degradation of a tropical forest to grasslands. Comparison with field data shows that this model can realistically simulate the land surface processes involving biospheric variations.

Zhang, Taiping↗

Design for and efficient dynamic climate model with realistic geography

The long term climate sensitivity which include realistic atmospheric dynamics are severely restricted by the expense of integrating atmospheric general circulation models are discussed. Taking as an example models used at GSFC for this dynamic model is an alternative which is of much lower horizontal or vertical resolution. The model of Heid and Suarez uses only two levels in the vertical and, although it has conventional grid resolution in the meridional direction, horizontal resolution is reduced by keeping only a few degrees of freedom in the zonal wavenumber spectrum. Without zonally asymmetric forcing this model simulates a day in roughly 1/2 second on a CRAY. The model under discussion is a fully finite differenced, zonally asymmetric version of the Heid-Suarez model. It is anticipated that speeds can be obtained a few seconds a day roughly 50 times faster than moderate resolution, multilayer GCM's.

Suarez, M. J.↗

Stochastic modeling of the time-advanced equations for climate dynamics

Long term, climatologically significant statistical properties of the atmospheric component of the climate system are examined in an effort to correctly and efficiently incorporate this component into larger models for climate that include oceanic and cryospheric influences and deal realistically with climate replication. The magnetic of the unpredictable (noise) component of long term atmospheric behavior is also studied in order to develop the potential for viable climatological forecasting.

Laurmann, J. A.↗

A streamlined approach to characterize microalgae strains for biomass productivity under dynamic climate simulation conditions

Screening microalgae strains under static light and temperature flask conditions cannot directly quantify the biomass productivities of algae growing in the dynamically fluctuating light and temperature conditions of outdoor ponds. In this effort, we describe a testing pipeline that screens for productivity under climate simulated conditions. A validated, miniaturized Laboratory Environmental Algae Pond Simulator (mini-LEAPS) photobioreactor was used to determine optimal medium salinities and biomass productivities of cold-tolerant microalgae collected from Arctic, subarctic, Antarctic, and subalpine habitats. Strains characterized in this effort include: Chlorella antarctica UTEX1959, Chlorella sp. UTEXSNO69, Chloromonas rosae UTEXSNO11, Phaeodactylum tricornutum UTEX646, and Stichococcus minutus CCALA727. Observed productivities of the characterized strains ranged from 1.74 ± 0.25 to 8.18 ± 0.81 g m –2 day –1 (as ash-free dry weight). For each strain, a temperature tolerance profile was generated to identify the most appropriate season (s) for cultivation. The two most promising strains, Chlorella sp. UTEX SNO69 and P. tricornutum UTEX646, were tested alongside the DISCOVR winter benchmark strain Monoraphidium minutum 26B-AM in outdoor open ponds at the PNNL Algae Testbed (PAT) in Arizona. Further, under cold-season outdoor pond conditions, Chlorella sp. UTEXSNO69 achieved average areal biomass productivities of 8.21 ± 0.50 g m –2 day –1 , not significantly different from that of the benchmark strain (8.52 ± 0.43 g m –2 day –1 , p > 0.05). Under spring outdoor pond conditions, P. tricornutum UTEX646 achieved average areal biomass productivities 10.34 ± 0.27 g m –2 day –1 , not significantly different from that of the benchmark (10.07 ± 0.50 g m –2 day –1 ) under the tested conditions (p > 0.05). The streamlined pipeline was thus demonstrated to successfully characterize productive microalgae strains for outdoor deployment by weeding out algae strains that were not productive under the tested dynamic light and temperature conditions.

59 BASIC BIOLOGICAL SCIENCES↗

Climate Dynamics Preceding Summer Forest Fires in California and the Extreme Case of 2018

Recent record-breaking wildfire seasons in California prompt an investigation into the climate patterns that typically precede anomalous summer burned forest area. Using burned-area data from the U.S. Forest Service’s Monitoring Trends in Burn Severity (MTBS) product and climate data from the fifth major global reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ERA5) over 1984–2018, relationships between the interannual variability of antecedent climate anomalies and July California burned area are spatially and temporally characterized. Lag correlations show that antecedent high vapor pressure deficit (VPD), high temperatures, frequent extreme high temperature days, low precipitation, high subsidence, high geopotential height, low soil moisture, and low snowpack and snowmelt anomalies all correlate significantly with July California burned area as far back as the January before the fire season. Seasonal regression maps indicate that a global midlatitude atmospheric wave train in late winter is associated with anomalous July California burned area. July 2018, a year with especially high burned area, was to some extent consistent with the general patterns revealed by the regressions: low winter precipitation and high spring VPD preceded the extreme burned area. However, geopotential height anomaly patterns were distinct from those in the regressions. Extreme July heat likely contributed to the extent of the fires ignited that month, even though extreme July temperatures do not historically significantly correlate with July burned area. While the 2018 antecedent climate conditions were typical of a high-burned-area year, they were not extreme, demonstrating the likely limits of statistical prediction of extreme fire seasons and the need for individual case studies of extreme years.

54 ENVIRONMENTAL SCIENCES↗

Climate dynamics experiments using a GCM simulations

The study of surface-atmosphere interactions has begun with studies of the effect of altering the ocean and land boundaries. A ten year simulation of global climate using observed sea surface temperature anomalies has begun using the NCAR Community Climate Model (CCM1). The results for low resolution (R15) were computed for the first 8 years of the simulation and compared with the observed surface temperatures and the MSU (Microwave Sounding Unit) observations of tropospheric temperature. A simulation at higher resolution (T42) was done to ascertain the effect of interactive soil hydrology on the system response to an El Nino sea surface temperature perturbation. Initial analysis of this simulations was completed.

Fitzjarrald, Dan↗

Data-driven modeling to enhance municipal water demand estimates in response to dynamic climate conditions

Altered precipitation and temperature patterns from a changing climate will affect supply, demand, and overall municipal water system operations throughout the arid western U.S. While supply forecasts leverage hydrological models to connect climate influences with surface water availability, demand forecasts typically estimate water use independent of climate and other externalities. Stemming from an increased focus on seasonal water demand management, we use the Salt Lake City, Utah municipal water system as a test bed to assess model accuracy versus complexity trade-offs between simple climate-independent econometric-based models and complex climate-sensitive data-driven models to average to extreme wet and dry climate conditions—representative of a new climate normal. Here, the climate-independent model displayed low performance during extreme dry conditions with predictions exceeding 90% and 40% of the observed monthly and seasonal volumetric demands, respectively, which we attribute to insufficient model complexity. The climate-sensitive models displayed greater accuracy in all conditions, with an ordinary least squares model demonstrating a measurable reduction in prediction bias (3.4% vs. -27.3%) and RMSE (74.0 lpcd vs. 294 lpcd) compared to the climate-independent model. The climate-sensitive workflow increased model accuracy and characterized climate-demand interactions, demonstrating a novel tool to enhance water system management.

54 ENVIRONMENTAL SCIENCES↗

Long-Term Arctic Peatland Dynamics, Vegetation and Climate History of the Pur-Taz Region, Western Siberia

Stratigraphic analyses of peat composition, LOI, pollen, spores, macrofossils, charcoal, and AMS ages are used to reconstruct the peatland, vegetation and climatic dynamics in the Pur-Taz region of western Siberia over 5000 years (9300 - 4500 BP). Section stratigraphy shows many changes from shallow lake sediment to different combinations of forested or open sedge, moss, and Equisetum fen and peatland environments. Macrofossil and pollen data indicate that Larix sibirica and Betula pubescens trees were first to arrive, followed by Picea obovata. The dominance of Picea macrofossils 6000-5000 BP in the Pur-Taz peatland along with regional Picea pollen maxima indicate warmer conditions and movement of the spruce treeline northward at this time. The decline of pollen and macrofossils from all of these tree species in uppermost peats suggests a change in the environment less favorable for their growth, perhaps cooler temperatures and/or less moisture. Of major significance is the evidence for old ages of the uppermost peats in this area of Siberia, suggesting a real lack of peat accumulation in recent millennia or recent oxidation of uppermost peat.

Peteet, Dorothy↗

On a simplified solution of climate-carbon dynamics in idealized flat10MIP simulations

Abstract. Idealized experiments with coupled climate-carbon Earth system models (ESMs) provide a basis for understanding the response of the carbon cycle to external forcing and for quantifying climate-carbon feedbacks. Here, we analyze globally-averaged results from idealized esm-flat10 experiments and show that most models exhibit a quasi-linear relationship between cumulative carbon uptake on land and in the ocean during a period of constant fossil fuel emissions of 10 Pg C yr−1. We hypothesize that this relationship does not depend on emission pathways. Further, as a simplification, we quantify the relationship between cumulative ocean carbon uptake and changes in ocean heat content using a linear approximation. In this way, changes in oceanic heat content and atmospheric CO2 concentration become interdependent variables, reducing the coupled temperature-CO2 system to just one differential equation. The equation can be solved analytically or numerically for the atmospheric CO2 concentration as a function of fossil fuel emissions. This approach leads to a simplified description of global carbon and climate dynamics, which could be used for applications beyond existing analytical frameworks.

Brovkin, Victor↗

Verification of vegetation maps made from remote sensing

Verification of vegetation maps is discussed, including a map of the vegetation of the Mt. Washington area of New Hampshire. This area was chosen to determine the accuracy of mapping by remote sensing at the boundary between two major forest biomass. Verification was carried out by ground observation and through the use of low altitude 70 mm infrared photographs. Two verification sampling schemes were used: a point method and a transect method. Resulting confidence limits gave an area weighted sampling accuracy of 89 pct. Spatial patterns in terrestrial vegetation must be understood in order to choose appropriate spatial resolutions required for remote sensing instruments, and to relate vegetation dynamics to climate dynamics and biogeochemical cycles.

Botkin, Daniel B.↗

The Mexican Drought Atlas: Tree-Ring Reconstructions of the Soil Moisture Balance During the Late Pre-Hispanic, Colonial, and Modern Eras

Mexico has suffered a long history and prehistory of severe sustained drought. Drought over Mexico is modulated by ocean-atmospheric variability in the Atlantic and Pacific, raising the possibility for long-range seasonal climate forecasting, which could help mediate the economic and social impacts of future dry spells. The instrumental record of Mexican climate is very limited before 1920, but tree-ring chronologies developed from old-growth forests in Mexico can provide an excellent proxy representation of the spatial pattern and intensity of past moisture regimes useful for the analysis of climate dynamics and climate impacts. The Mexican Drought Atlas (MXDA) has been developed from an extensive network of 252 climate sensitive tree-ring chronologies in and near Mexico. The MXDA reconstructions extend from 1400 CE-2012 and were calibrated with the instrumental summer (JJA) self-calibrating Palmer Drought Severity Index (scPDSI) on a 0.5deg latitude/longitude grid extending over land areas from 14 to 34degN and 75-120degW using Ensemble Point-by-Point Regression (EPPR) for the 1944-1984 period. The grid point reconstructions were validated for the period 1920-1943 against instrumental gridded scPDSI values based on the fewer weather station observations available during that interval. The MXDA provides a new spatial perspective on the historical impacts of moisture extremes over Mexico during the past 600-years, including the Aztec Drought of One Rabbit in 1454, the drought of El Ano de Hambre in 1785-1786, and the drought that preceded the Mexican Revolution of 1909-1910. The El Nino/Southern Oscillation (ENSO) is the most important ocean-atmospheric forcing of moisture variability detected with the MXDA. In fact, the reconstructions suggest that the strongest central equatorial Pacific sea surface temperature (SST) teleconnection to the soil moisture balance over North America may reside in northern Mexico. This ENSO signal has stronger and more time-stable correlations than computed for either the Atlantic Multidecadal Oscillation or Pacific Decadal Oscillation. The extended Multivariate ENSO Index is most highly correlated with reconstructed scPDSI over northern Mexico, where warm events favor moist conditions during the winter, spring, and early summer. This ENSO teleconnection to northern Mexico has been strong over the past 150 years, but it has been comparatively weak and non-stationary in the MXDA over central and southern Mexico where eastern tropical Pacific and Caribbean/tropical Atlantic SSTs seem to be more important. The ENSO teleconnection to northern Mexico is weaker in the available instrumental PDSI, but analyses based on the millennium climate simulations with the Community Earth System Model suggest that the moisture balance during the winter, spring, and early summer over northern Mexico may indeed be particularly sensitive to ENSO forcing. Nationwide drought is predicted to become more common with anthropogenic climate change, but the MXDA reconstructions indicate that intense "All Mexico" droughts have been rare over the past 600 years and their frequency does not appear to have increased substantially in recent decades.

El Nino↗

Possible effects of anthropogenically-increased CO2 on the dynamics of climate - Implications for ice age cycles

A dynamical model, developed to account for the observed major variations of global ice mass and atmospheric CO2 during the late Cenozoic, is used to provide a quantitative demonstration of the possibility that the anthropogenically-forced increase of atmospheric CO2, if maintained over a long period of time (perhaps by tectonic forcing), could displace the climatic system from an unstable regime of oscillating ice ages into a more stable regime representative of the pre-Pleistocene. This stable regime is characterized by orbitally-forced oscillations that are of much weaker amplitude than prevailed during the Pleistocene.

Saltzman, Barry↗

Understanding the Cascade: Removing GCM Biases Improves Dynamically Downscaled Climate Projections

Polarization surrounding bias correction (BC) in creating climate projections arises from its lack of physicality. Here, we perform and analyze 18 dynamical downscaling simulations (with and without BC) to better understand the physical impacts of BC, applied before downscaling, on regional climate output across the western United States. Without BC, downscaled precipitation is systematically and unrealistically wet biased compared to a hierarchy of observationally based datasets over the 1980–2014 period due to cascading mean–state Global Climate Model (GCM) biases: (a) overly strong lower–tropospheric lapse rates (5 K/km), (b) overly cold (2 K) tropospheric temperatures, and (c) anomalous mid–tropospheric cyclonic vorticity advection. With BC, downscaled precipitation (snow) biases are virtually eliminated (halved). Identified GCM biases are common to the broader Coupled Model Intercomparison Project ensemble. Physical effects of BC on the quality of the regionalized projections, pending an evaluation of BC's distortion of the downscaled climate response, may motivate its broader application by dynamical downscalers.

54 ENVIRONMENTAL SCIENCES↗

Seasonal Drought Prediction in East Africa: Can National Multi-Model Ensemble Forecasts Help?

The increasing food and water demands of East Africa's growing population are stressing the region's inconsistent water resources and rain-fed agriculture. As recently as in 2011 part of this region underwent one of the worst famine events in its history. Timely and skillful drought forecasts at seasonal scale for this region can inform better water and agro-pastoral management decisions, support optimal allocation of the region's water resources, and mitigate socio-economic losses incurred by droughts. However seasonal drought prediction in this region faces several challenges. Lack of skillful seasonal rainfall forecasts; the focus of this presentation, is one of those major challenges. In the past few decades, major strides have been taken towards improvement of seasonal scale dynamical climate forecasts. The National Centers for Environmental Prediction's (NCEP) National Multi-model Ensemble (NMME) is one such state-of-the-art dynamical climate forecast system. The NMME incorporates climate forecasts from 6+ fully coupled dynamical models resulting in 100+ ensemble member forecasts. Recent studies have indicated that in general NMME offers improvement over forecasts from any single model. However thus far the skill of NMME for forecasting rainfall in a vulnerable region like the East Africa has been unexplored. In this presentation we report findings of a comprehensive analysis that examines the strength and weakness of NMME in forecasting rainfall at seasonal scale in East Africa for all three of the prominent seasons for the region. (i.e. March-April-May, July-August-September and October-November- December). Simultaneously we also describe hybrid approaches; that combine statistical approaches with NMME forecasts; to improve rainfall forecast skill in the region when raw NMME forecasts lack in skill.

Shukla, Shraddhanand↗