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USGS2018

On the exchange of sensible and latent heat between the atmosphere and melting snow

The snow energy balance is difficult to measure during the snowmelt period, yet critical for predictions of water yield in regions characterized by snow cover. Robust simplifications of the snowmelt energy balance can aid our understanding of water resources in a changing climate. Research to date has demonstrated that the net turbulent flux ( F T ) between a melting snowpack and the atmosphere is negligible if the sum of atmospheric vapor pressure ( e a ) and temperature ( T a ) equals a constant, but it is unclear how frequently this situation holds across different sites. Here, we quantified the contribution of F T to the snowpack energy balance during 59 snowmelt periods across 11 sites in the FLUXNET2015 database with a detailed analysis of snowmelt in subarctic tundra near Abisko, Sweden. At the Abisko site we investigated the frequency of occurrences during which sensible heat flux ( H ) and latent heat flux ( λE ) are of (approximately) equal but opposite sign, and if the sum of these terms, F T , is therefore negligible during the snowmelt period. H approximately equaled - λE for less than 50% of the melt period and F T was infrequently a trivial term in the snowmelt energy balance at Abisko. The reason is that the relationship between observed e a and T a is roughly orthogonal to the “line of equality” at which H equals - λE as warmer T a during the melt period usually resulted in greater e a . This relationship holds both within melt periods at individual sites and across different sites in the FLUXNET2015 database, where F T comprised less than 20% of the energy available to melt snow, Q m , in 44% of the snowmelt periods studied here. F T / Q m was significantly related to the mean e a during the melt period, but not mean T a , and F T tended to be near 0 W m −2 when e a averaged ca . 0.5 kPa. F T may become an increasingly important term in the snowmelt energy balance across many global regions as warmer temperatures are projected to cause snow to melt more slowly and earlier in the year under conditions of lower net radiation ( R n ). Eddy covariance research networks such as Ameriflux must improve their ability to observe cold-season processes to enhance our understanding of water resources and surface-atmosphere exchange in a changing climate.

Agricultural and Forest Meteorology↗
USGS2012

An assessment of the carbon balance of arctic tundra: comparisons among observations, process models, and atmospheric inversions

Although arctic tundra has been estimated to cover only 8% of the global land surface, the large and potentially labile carbon pools currently stored in tundra soils have the potential for large emissions of carbon (C) under a warming climate. These emissions as radiatively active greenhouse gases in the form of both CO 2 and CH 4 could amplify global warming. Given the potential sensitivity of these ecosystems to climate change and the expectation that the Arctic will experience appreciable warming over the next century, it is important to assess whether responses of C exchange in tundra regions are likely to enhance or mitigate warming. In this study we compared analyses of C exchange of Arctic tundra between 1990–1999 and 2000–2006 among observations, regional and global applications of process-based terrestrial biosphere models, and atmospheric inversion models. Syntheses of the compilation of flux observations and of inversion model results indicate that the annual exchange of CO 2 between arctic tundra and the atmosphere has large uncertainties that cannot be distinguished from neutral balance. The mean estimate from an ensemble of process-based model simulations suggests that arctic tundra acted as a sink for atmospheric CO 2 in recent decades, but based on the uncertainty estimates it cannot be determined with confidence whether these ecosystems represent a weak or a strong sink. Tundra was 0.6 °C warmer in the 2000s compared to the 1990s. The central estimates of the observations, process-based models, and inversion models each identify stronger sinks in the 2000s compared with the 1990s. Similarly, the observations and the applications of regional process-based models suggest that CH 4 emissions from arctic tundra have increased from the 1990s to 2000s. Based on our analyses of the estimates from observations, process-based models, and inversion models, we estimate that arctic tundra was a sink for atmospheric CO 2 of 110 Tg C yr -1 (uncertainty between a sink of 291 Tg C yr -1 and a source of 80 Tg C yr -1 ) and a source of CH 4 to the atmosphere of 19 Tg C yr -1 (uncertainty between sources of 8 and 29 Tg C yr -1 ). The suite of analyses conducted in this study indicate that it is clearly important to reduce uncertainties in the observations, process-based models, and inversions in order to better understand the degree to which Arctic tundra is influencing atmospheric CO 2 and CH 4 concentrations. The reduction of uncertainties can be accomplished through (1) the strategic placement of more CO 2 and CH 4 monitoring stations to reduce uncertainties in inversions, (2) improved observation networks of ground-based measurements of CO 2 and CH 4 exchange to understand exchange in response to disturbance and across gradients of hydrological variability, and (3) the effective transfer of information from enhanced observation networks into process-based models to improve the simulation of CO 2 and CH 4 exchange from arctic tundra to the atmosphere.

Biogeosciences↗
USGS2018

Paleoclimate Records: Providing context and understanding of current Arctic change

At present, the Arctic Ocean is experiencing changes in ocean surface temperature and sea ice extent that are unprecedented in the era of satellite observations, which extend from the 1980s to the present (see sections 5c,d). To provide context for current changes, scientists turn to paleoclimate records to document and study anthropogenic influence and natural decadal and multidecadal climate variability in the Arctic system. Paleoceanographic records extend limited Arctic instrumental measurements back in time and are central to improving our understanding of climate dynamics and the predictive capability of climate models. By comparing paleoceanographic records with modern observations, scientists can place the rates and magnitudes of modern Arctic change in the context of those inferred from the geological record. Over geological time, paleoceanographic reconstructions using, for instance, marine sediment cores indicate that the Arctic has experienced huge sea ice fluctuations. These fluctuations range from nearly completely ice-free to totally ice-covered conditions. The appearance of ice-rafted debris and sea ice-dependent diatoms in Arctic marine sediments indicate that the first Arctic sea ice formed approxi-mately 47 million years ago (St. John 2008; Stickley et al. 2009; Fig. SB5.1), coincident with an interval of declining atmospheric carbon dioxide (CO2) concentration, global climate cooling, and expansion of Earths cryosphere during the middle Eocene. The development of year-round (i.e., perennial) sea ice in the central Arctic Ocean, similar to conditions that exist today, is evident in sediment records as early as 1418 million years ago (Darby 2008). These records suggest that transitions in sea ice cover occur over many millennia and often vary in concert with the waxing and waning of circum-Arctic land ice sheets, ice shelves, and long-term fluctuations in ocean and atmospheric temperature and atmospheric CO2 concentrations (Stein et al. 2012; Jakobsson et al. 2014). Over shorter time scales, shallow sediment records from Arctic Ocean continental shelves allow more detailed, higher-resolution (hundreds of years resolution) reconstructions of sea ice history extending through the Holocene (11 700 years ago to present), the most recent interglacial period. A notable feature of these records is an early Holocene sea ice minimum, corresponding to a thermal maximum (warm) period from 11 000 to 5000 years ago, when the Arctic may have been warmer and had less summertime sea ice than today (Kaufman et al. 2004). However, it is not clear that the Arctic was ice-free at any point during the Holocene (Polyak et al. 2010). High-resolution paleosea ice records from the western Arctic in the Chukchi and East Siberian Seas indicate that sea ice concentrations increased through the Holocene in concert with decreasing summer solar insolation (sunlight). Sea ice extent in this region also varied in response to the volume of Pacific water delivered via the Bering Strait into the Arctic Basin (Stein et al. 2017; Polyak et al. 2016). Records from the Fram Strait (Mller et al. 2012), Laptev Sea (Hrner et al. 2016), and Canadian Arctic Archipelago (Vare et al. 2009) also indicate a similar long-term expansion of sea ice and suggest sea ice extent in these regions is modulated by the varying influx of warm Atlantic water into the Arctic Basin (e.g., Werner et al. 2013). Taken together, available records support a circum-Arctic sea ice expansion during the late Holocene. A notably high-resolution summer sea ice history (<5-year resolution) has been established for the last 1450 years using a network of terrestrial records (tree ring , lake sediment, and ice core records) located around the margins of the Arctic Ocean (Kinnard et al. 2011). Results summarized in Fig. SB5.2 indicate a pronounced decline in summer sea ice extent beginning in the 20th century, with exceptionally low ice extent recorded since the mid-1990s, consistent with the satellite record (see section 5d). While several episodes of reduced and expanded sea ice extent occur in association with climate anomalies such as the Medieval Climate Warm Period (AD 8001300) and the Little Ice Age (AD 14501850), the magnitude and pace of the modern decline in sea ice is outside of the range of natural variability and unprecedented in the 1450-year reconstruction (Kinnard et al. 2011). A radiocarbon-dated driftwood record of the Ellesmere ice shelf in the Canadian High Arctic, the oldest landfast ice in the Northern Hemisphere, also demonstrates a substantial reduction in ice extents over the 20th century (England et al. 2017). A supporting sediment record indicates that inflowing Atlantic water in Fram Strait has warmed by 2C since 1900, driving break up and melt of sea ice (Spielhagen et al. 2011). Complementary mooring and satellite observations show the Atlantification of the eastern Arctic due to enhanced inflow of warm saline water through Fram Strait (Nilsen et al. 2016) and nutrient-rich Pacific water via the Bering has increased by more than 50% (Woodgate et al. 2012), further driving sea ice melt and warming seas. Similar high-resolution proxy records from Arctic regions also indicate that the modern rate of increasing annual surface air temperatures has not been observed over at least the last 2000 years (McKay and Kaufman 2014). Scientists conclude that broad-scale sea ice variations recorded in the paleo record were dominantly driven by changes in basin-scale changes in atmospheric circulation patterns, fluctuations in air temperature, strength of incoming solar radiation, and changes in the inflow of warm water via Pacific and Atlantic inflows (Polyak et al. 2010). There is general consensus that ice-free Arctic summers are likely before the end of the 21st century (e.g., Stroeve et al. 2007; Massonnet et al. 2012), while some climate model projections suggest ice-free Arctic summers as early as 2030 (Wang and Overland 2009). Paleoclimate studies and observational time series attribute the decline in sea ice extent and thickness over the last decade to both enhanced greenhouse warming and natural climate variability. While understanding the interplay of these factors is critical for future projections of Arctic sea ice and ecosystems, most observational time series records cover only a few decades. This highlights the need for additional paleoceanographic reconstructions across multiple spatial and temporal domains to better understand the drivers and implications of present and future Arctic Ocean change.

Bulletin American Meteorological Society↗
USGS2025

Global methane budget 2000-2020

Understanding and quantifying the global methane (CH 4 ) budget is important for assessing realistic pathways to mitigate climate change. CH 4 is the second most important human-influenced greenhouse gas in terms of climate forcing after carbon dioxide (CO 2 ), and both emissions and atmospheric concentrations of CH 4 have continued to increase since 2007 after a temporary pause. The relative importance of CH 4 emissions compared to those of CO 2 for temperature change is related to its shorter atmospheric lifetime, stronger radiative effect, and acceleration in atmospheric growth rate over the past decade, the causes of which are still debated. Two major challenges in quantifying the factors responsible for the observed atmospheric growth rate arise from diverse, geographically overlapping CH 4 sources and from the uncertain magnitude and temporal change in the destruction of CH 4 by short-lived and highly variable hydroxyl radicals (OH). To address these challenges, we have established a consortium of multidisciplinary scientists under the umbrella of the Global Carbon Project to improve, synthesise, and update the global CH 4 budget regularly and to stimulate new research on the methane cycle. Following Saunois et al. (2016, 2020), we present here the third version of the living review paper dedicated to the decadal CH 4 budget, integrating results of top-down CH 4 emission estimates (based on in situ and Greenhouse Gases Observing SATellite (GOSAT) atmospheric observations and an ensemble of atmospheric inverse-model results) and bottom-up estimates (based on process-based models for estimating land surface emissions and atmospheric chemistry, inventories of anthropogenic emissions, and data-driven extrapolations). We present a budget for the most recent 2010–2019 calendar decade (the latest period for which full data sets are available), for the previous decade of 2000–2009 and for the year 2020. The revision of the bottom-up budget in this 2025 edition benefits from important progress in estimating inland freshwater emissions, with better counting of emissions from lakes and ponds, reservoirs, and streams and rivers. This budget also reduces double counting across freshwater and wetland emissions and, for the first time, includes an estimate of the potential double counting that may exist (average of 23 Tg CH 4 yr −1 ). Bottom-up approaches show that the combined wetland and inland freshwater emissions average 248 [159–369] Tg CH 4 yr −1 for the 2010–2019 decade. Natural fluxes are perturbed by human activities through climate, eutrophication, and land use. In this budget, we also estimate, for the first time, this anthropogenic component contributing to wetland and inland freshwater emissions. Newly available gridded products also allowed us to derive an almost complete latitudinal and regional budget based on bottom-up approaches. For the 2010–2019 decade, global CH 4 emissions are estimated by atmospheric inversions (top-down) to be 575 Tg CH 4 yr −1 (range 553–586, corresponding to the minimum and maximum estimates of the model ensemble). Of this amount, 369 Tg CH 4 yr −1 or ∼ 65 % is attributed to direct anthropogenic sources in the fossil, agriculture, and waste and anthropogenic biomass burning (range 350–391 Tg CH 4 yr −1 or 63 %–68 %). For the 2000–2009 period, the atmospheric inversions give a slightly lower total emission than for 2010–2019, by 32 Tg CH 4 yr −1 (range 9–40). The 2020 emission rate is the highest of the period and reaches 608 Tg CH 4 yr −1 (range 581–627), which is 12 % higher than the average emissions in the 2000s. Since 2012, global direct anthropogenic CH 4 emission trends have been tracking scenarios that assume no or minimal climate mitigation policies proposed by the Intergovernmental Panel on Climate Change (shared socio-economic pathways SSP5 and SSP3). Bottom-up methods suggest 16 % (94 Tg CH 4 yr −1 ) larger global emissions (669 Tg CH 4 yr −1 , range 512–849) than top-down inversion methods for the 2010–2019 period. The discrepancy between the bottom-up and the top-down budgets has been greatly reduced compared to the previous differences (167 and 156 Tg CH 4 yr −1 in Saunois et al. (2016, 2020) respectively), and for the first time uncertainties in bottom-up and top-down budgets overlap. Although differences have been reduced between inversions and bottom-up, the most important source of uncertainty in the global CH 4 budget is still attributable to natural emissions, especially those from wetlands and inland freshwaters. The tropospheric loss of methane, as the main contributor to methane lifetime, has been estimated at 563 [510–663] Tg CH 4 yr −1 based on chemistry–climate models. These values are slightly larger than for 2000–2009 due to the impact of the rise in atmospheric methane and remaining large uncertainty ( ∼ 25 %). The total sink of CH 4 is estimated at 633 [507–796] Tg CH 4 yr −1 by the bottom-up approaches and at 554 [550–567] Tg CH 4 yr −1 by top-down approaches. However, most of the top-down models use the same OH distribution, which introduces less uncertainty to the global budget than is likely justified. For 2010–2019, agriculture and waste contributed an estimated 228 [213–242] Tg CH 4 yr −1 in the top-down budget and 211 [195–231] Tg CH 4 yr −1 in the bottom-up budget. Fossil fuel emissions contributed 115 [100–124] Tg CH 4 yr −1 in the top-down budget and 120 [117–125] Tg CH 4 yr −1 in the bottom-up budget. Biomass and biofuel burning contributed 27 [26–27] Tg CH 4 yr −1 in the top-down budget and 28 [21–39] Tg CH 4 yr −1 in the bottom-up budget. We identify five major priorities for improving the CH 4 budget: (i) producing a global, high-resolution map of water-saturated soils and inundated areas emitting CH 4 based on a robust classification of different types of emitting ecosystems; (ii) further development of process-based models for inland-water emissions; (iii) intensification of CH 4 observations at local (e.g. FLUXNET-CH 4 measurements, urban-scale monitoring, satellite imagery with pointing capabilities) to regional scales (surface networks and global remote sensing measurements from satellites) to constrain both bottom-up models and atmospheric inversions; (iv) improvements of transport models and the representation of photochemical sinks in top-down inversions; and (v) integration of 3D variational inversion systems using isotopic and/or co-emitted species such as ethane as well as information in the bottom-up inventories on anthropogenic super-emitters detected by remote sensing (mainly oil and gas sector but also coal, agriculture, and landfills) to improve source partitioning.

Earth System Science Data↗