Cooperative Institute for Research in the Atmosphere

About: Cooperative Institute for Research in the Atmosphere is a based out in . It is known for research contribution in the topics: Snow & Data assimilation. The organization has 332 authors who have published 997 publications receiving 38835 citations. The organization is also known as: CIRA.

An application of upscaled optimal foraging theory using hidden Markov modelling: year-round behavioural variation in a large arctic herbivore

Abstract: In highly seasonal environments, animals face critical decisions regarding time allocation, diet optimisation, and habitat use. In the Arctic, the short summers are crucial for replenishing body reserves, while low food availability and increased energetic demands characterise the long winters (9–10 months). Under such extreme seasonal variability, even small deviations from optimal time allocation can markedly impact individuals’ condition, reproductive success and survival. We investigated which environmental conditions influenced daily, seasonal, and interannual variation in time allocation in high-arctic muskoxen (Ovibos moschatus) and evaluated whether results support qualitative predictions derived from upscaled optimal foraging theory. Using hidden Markov models (HMMs), we inferred behavioural states (foraging, resting, relocating) from hourly positions of GPS-collared females tracked in northeast Greenland (28 muskox-years). To relate behavioural variation to environmental conditions, we considered a wide range of spatially and/or temporally explicit covariates in the HMMs. While we found little interannual variation, daily and seasonal time allocation varied markedly. Scheduling of daily activities was distinct throughout the year except for the period of continuous daylight. During summer, muskoxen spent about 69% of time foraging and 19% resting, without environmental constraints on foraging activity. During winter, time spent foraging decreased to 45%, whereas about 43% of time was spent resting, mediated by longer resting bouts than during summer. Our results clearly indicate that female muskoxen follow an energy intake maximisation strategy during the arctic summer. During winter, our results were not easily reconcilable with just one dominant foraging strategy. The overall reduction in activity likely reflects higher time requirements for rumination in response to the reduction of forage quality (supporting an energy intake maximisation strategy). However, deep snow and low temperatures were apparent constraints to winter foraging, hence also suggesting attempts to conserve energy (net energy maximisation strategy). Our approach provides new insights into the year-round behavioural strategies of the largest Arctic herbivore and outlines a practical example of how to approximate qualitative predictions of upscaled optimal foraging theory using multi-year GPS tracking data.

East Greenland freshwater runoff to the Greenland-Iceland-Norwegian Seas 1999–2004 and 2071–2100

Abstract: In this paper, we quantify the terrestrial flux of freshwater runoff from East Greenland to the Greenland-Iceland-Norwegian (GIN) Seas for the periods 1999–2004 and 2071–2100. Our analysis includes separate calculations of runoff from the Greenland Ice Sheet (GrIS) and the land strip area between the GrIS and the ocean. This study is based on validation and calibration of SnowModel with in situ data from the only two long-term permanent automatic meteorological and hydrometric monitoring catchments in East Greenland: the Mittivakkat Glacier catchment (65°N) in SE Greenland, and the Zackenberg Glacier catchment (74°N) in NE Greenland. SnowModel was then used to estimate runoff from all of East Greenland to the ocean. Modelled glacier recession in both catchments for the period 1999–2004 was in accordance with observations, and dominates the annual catchment runoff by 30–90%. Average runoff from Mittivakkat, ∼3·7 × 10−2 km3 y−1, and Zackenberg, ∼21·9 × 10−2 km3 y−1, was dominated by the percentage of catchment glacier cover. Modelled East Greenland freshwater input to the North Atlantic Ocean was ∼440 km3 y−1 (1999–2004), dominated by contributions of ∼40% from the land strip area and ∼60% from the GrIS. East Greenland runoff contributes ∼10% of the total annual freshwater export from the Arctic Ocean to the Greenland Sea. The future (2071–2100) climate impact assessment based on the Intergovernmental Panel on Climate Change (IPCC) A2 and B2 scenarios indicates an increase of mean annual East Greenland air temperature by 2·7 °C from today's values. For 2071–2100, the mean annual freshwater input to the North Atlantic Ocean is modelled to be ∼650 km3 y−1: ∼30% from the land strip area and ∼70% from the GrIS. This is an increase of approximately ∼50% from today's values. The freshwater runoff from the GrIS is more than double from today's values, based largely on increasing air temperature rather than from changes in net precipitation. Copyright © 2008 John Wiley & Sons, Ltd.

Ensemble-based satellite-derived carbon dioxide and methane column-averaged dry-air mole fraction data sets (2003--2018) for carbon and climate applications

Abstract: . Satellite retrievals of column-averaged dry-air mole fractions of carbon dioxide ( CO2 ) and methane ( CH4 ), denoted XCO2 and XCH4 , respectively, have been used in recent years to obtain information on natural and anthropogenic sources and sinks and for other applications such as comparisons with climate models. Here we present new data sets based on merging several individual satellite data products in order to generate consistent long-term climate data records (CDRs) of these two Essential Climate Variables (ECVs). These ECV CDRs, which cover the time period 2003–2018, have been generated using an ensemble of data products from the satellite sensors SCIAMACHY/ENVISAT and TANSO-FTS/GOSAT and (for XCO2 ) for the first time also including data from the Orbiting Carbon Observatory 2 (OCO-2) satellite. Two types of products have been generated: (i) Level 2 (L2) products generated with the latest version of the ensemble median algorithm (EMMA) and (ii) Level 3 (L3) products obtained by gridding the corresponding L2 EMMA products to obtain a monthly 5 ∘ × 5 ∘ data product in Obs4MIPs (Observations for Model Intercomparisons Project) format. The L2 products consist of daily NetCDF (Network Common Data Form) files, which contain in addition to the main parameters, i.e., XCO2 or XCH4 , corresponding uncertainty estimates for random and potential systematic uncertainties and the averaging kernel for each single (quality-filtered) satellite observation. We describe the algorithms used to generate these data products and present quality assessment results based on comparisons with Total Carbon Column Observing Network (TCCON) ground-based retrievals. We found that the XCO2 Level 2 data set at the TCCON validation sites can be characterized by the following figures of merit (the corresponding values for the Level 3 product are listed in brackets) – single-observation random error ( 1σ ): 1.29 ppm (monthly: 1.18 ppm); global bias: 0.20 ppm (0.18 ppm); and spatiotemporal bias or relative accuracy ( 1σ ): 0.66 ppm (0.70 ppm). The corresponding values for the XCH4 products are single-observation random error ( 1σ ): 17.4 ppb (monthly: 8.7 ppb); global bias: −2.0 ppb ( −2.9 ppb); and spatiotemporal bias ( 1σ ): 5.0 ppb (4.9 ppb). It has also been found that the data products exhibit very good long-term stability as no significant long-term bias trend has been identified. The new data sets have also been used to derive annual XCO2 and XCH4 growth rates, which are in reasonable to good agreement with growth rates from the National Oceanic and Atmospheric Administration (NOAA) based on marine surface observations. The presented ECV data sets are available (from early 2020 onwards) via the Climate Data Store (CDS, https://cds.climate.copernicus.eu/ , last access: 10 January 2020) of the Copernicus Climate Change Service (C3S, https://climate.copernicus.eu/ , last access: 10 January 2020).

A temporally adaptive classifier for multispectral imagery

Abstract: This paper presents a new temporally adaptive classification system for multispectral images. A spatial-temporal adaptation mechanism is devised to account for the changes in the feature space as a result of environmental variations. Classification based upon spatial features is performed using Bayesian framework or probabilistic neural networks (PNNs) while the temporal updating takes place using a spatial-temporal predictor. A simple iterative updating mechanism is also introduced for adjusting the parameters of these systems. The proposed methodology is used to develop a pixel-based cloud classification system. Experimental results on cloud classification from satellite imagery are provided to show the usefulness of this system.