An introduction to Trends in Extreme Weather and Climate Events: Observations, Socioeconomic Impacts, Terrestrial Ecological Impacts, and Model Projections

Weather and climatic extremes can have serious and damaging effects on human society and infrastructure as well as on ecosystems and wildlife. Thus, they are usually the main focus of attention of the news media in reports on climate. There are some indications from observations concerning how climatic extremes may have changed in the past. Climate models show how they could change in the future either due to natural climate fluctuations or under conditions of greenhouse gas-induced warming. These observed and modeled changes relate directly to the understanding of socioeconomic and ecological impacts related to extremes.Integrative Biolog

Northern Eurasia Future Initiative (NEFI): facing the challenges and pathways of global change in the twenty-first century

During the past several decades, the Earth system has changed significantly, especially across Northern Eurasia. Changes in the socio-economic conditions of the larger countries in the region have also resulted in a variety of regional environmental changes that can have global consequences. The Northern Eurasia Future Initiative (NEFI) has been designed as an essential continuation of the Northern Eurasia Earth Science Partnership Initiative (NEESPI), which was launched in 2004. NEESPI sought to elucidate all aspects of ongoing environmental change, to inform societies and, thus, to better prepare societies for future developments. A key principle of NEFI is that these developments must now be secured through science-based strategies co-designed with regional decision-makers to lead their societies to prosperity in the face of environmental and institutional challenges. NEESPI scientific research, data, and models have created a solid knowledge base to support the NEFI program. This paper presents the NEFI research vision consensus based on that knowledge. It provides the reader with samples of recent accomplishments in regional studies and formulates new NEFI science questions. To address these questions, nine research foci are identified and their selections are briefly justified. These foci include warming of the Arctic; changing frequency, pattern, and intensity of extreme and inclement environmental conditions; retreat of the cryosphere; changes in terrestrial water cycles; changes in the biosphere; pressures on land use; changes in infrastructure; societal actions in response to environmental change; and quantification of Northern Eurasia’s role in the global Earth system. Powerful feedbacks between the Earth and human systems in Northern Eurasia (e.g., mega-fires, droughts, depletion of the cryosphere essential for water supply, retreat of sea ice) result from past and current human activities (e.g., large-scale water withdrawals, land use, and governance change) and potentially restrict or provide new opportunities for future human activities. Therefore, we propose that integrated assessment models are needed as the final stage of global change assessment. The overarching goal of this NEFI modeling effort will enable evaluation of economic decisions in response to changing environmental conditions and justification of mitigation and adaptation efforts

The Northern Eurasia Earth Science Partnership: An Example of Science Applied to Societal Needs

Northern Eurasia, the largest landmass in the northern extratropics, accounts for ~20% of the global land area. However, little is known about how the biogeochemical cycles, energy and water cycles, and human activities specific to this carbon-rich, cold region interact with global climate. A major concern is that changes in the distribution of land-based life, as well as its interactions with the environment, may lead to a self-reinforcing cycle of accelerated regional and global warming. With this as its motivation, the Northern Eurasian Earth Science Partnership Initiative (NEESPI) was formed in 2004 to better understand and quantify feedbacks between northern Eurasian and global climates. The first group of NEESPI projects has mostly focused on assembling regional databases, organizing improved environmental monitoring of the region, and studying individual environmental processes. That was a starting point to addressing emerging challenges in the region related to rapidly and simultaneously changing climate, environmental, and societal systems. More recently, the NEESPI research focus has been moving toward integrative studies, including the development of modeling capabilities to project the future state of climate, environment, and societies in the NEESPI domain. This effort will require a high level of integration of observation programs, process studies, and modeling across disciplines

Temporal Changes in the Observed Relationship between Cloud Cover and Surface Air Temperature

The relationship between cloud cover and near-surface air temperature and its decadal changes are examined using the hourly synoptic data for the past four to six decades from five regions of the Northern Hemisphere: Canada, the United States, the former Soviet Union, China, and tropical islands of the western Pacific. The authors define the normalized cloud cover–surface air temperature relationship, NOCET or dT/dCL, as a temperature anomaly with a unit (one-tenth) deviation of total cloud cover from its average value. Then mean monthly NOCET time series (night- and daytime, separately) are area-averaged and parameterized as functions of surface air humidity and snow cover. The day- and nighttime NOCET variations are strongly anticorrelated with changes in surface humidity. Furthermore, the daytime NOCET changes are positively correlated to changes in snow cover extent. The regionally averaged nighttime NOCET varies from 20.05 K tenth21 in the wet Tropics to 1.0 K tenth21 at midlatitudes in winter. The daytime regional NOCET ranges from 20.4 K tenth21 in the Tropics to 0.7 K tenth21 at midlatitudes in winter. The authors found a general strengthening of a daytime surface cooling during the post–World War II period associated with cloud cover over the United States and China, but a minor reduction of this cooling in higher latitudes. Furthermore, since the 1970s, a prominent increase in atmospheric humidity has significantly weakened the effectiveness of the surface warming (best seen at nighttime) associated with cloud cover. The authors apportion the spatiotemporal field of interactions between total cloud cover and surface air temperature into a bivariate relationship (described by two equations, one for daytime and one for nighttime) with surface air humidity and snow cover and two constant factors. These factors are invariant in space and time domains. It is speculated that they may represent empirical estimates of the overall cloud cover effect on the surface air temperature

The Relationship of Cloud Cover to Near-Surface Temperature and Humidity: Comparison of GCM Simulations with Empirical Data

One of the possible ways to check the adequacy of the physical description of meteorological elements in global climate models (GCMs) is to compare the statistical structure of these elements reproduced by models with empirical data from the world climate observational system. The success in GCM development warranted a further step in this assessment. The description of the meteorological element in the model can be considered adequate if, with a proper reproduction of the mean and variability of this element (as shown by the observational system), the model properly reproduces the internal relationships between this element and other climatic variables (as observed during the past several decades). Therefore, to distinguish more reliable models, the authors suggest first analyzing these relationships, ‘‘the behavior of the climatic system,’’ using observational data and then testing the GCMs’ output against this behavior. In this paper, the authors calculated a set of statistics from synoptic data of the past several decades and compared them with the outputs of seven GCMs participating in the Atmospheric Model Intercomparison Project (AMIP), focusing on cloud cover, one of the major trouble spots for which parameterizations are still not well established, and its interaction with other meteorological fields. Differences between long-term mean values of surface air temperature and atmospheric humidity for average and clear sky or for average and overcast conditions characterize the long-term noncausal associations between these two elements and total cloud cover. Not all the GCMs reproduce these associations properly. For example, there was a general agreement in reproducing mean daily cloud–temperature associations in the cold season among all models tested, but large discrepancies between empirical data and some models are found for summer conditions. A correct reproduction of the diurnal cycle of cloud–temperature associations in the warm season is still a major challenge for two of the GCMs that were tested

Relationships between recent pan-arctic snow cover and hydroclimate trends

Using the Variable Infiltration Capacity (VIC) land surface model forced with gridded climatic observations, the authors reproduce spatial and temporal variations of snow cover extent (SCE) reported by the National Oceanic and Atmospheric Administration (NOAA) Northern Hemisphere weekly satellite SCE data. Both observed and modeled North American and Eurasian snow cover in the pan-Arctic have statistically significant negative trends from April through June over the period 1972-2006. To diagnose the causes of the pan-Arctic SCE recession, the authors identify the role of surface energy fluxes generated in VIC and assess the relationships between 15 hydroclimatic indicators and NOAA SCE observations over each snowcovered sensitivity zone (SCSZ) for both North America and Eurasia. The authors find that surface net radiation (SNR) provides the primary energy source and sensible heat (SH) plays a secondary role in observed changes of SCE. As compared with SNR and SH, latent heat has only a minor influence on snow cover changes. In addition, these changes in surface energy fluxes resulting in the pan-Arctic snow cover recession are mainly driven by statistically significant decreases in snow surface albedo and increased air temperatures (surface air temperature, daily maximum temperature, and daily minimum temperature), as well as statistically significant increased atmospheric water vapor pressure. Contributions of other hydroclimate variables that the authors analyzed (downward shortwave radiation, precipitation, diurnal temperature range, wind speed, and cloud cover) are not significant for observed SCE changes in either the North American or Eurasian SCSZs

The role of surface energy fluxes in pan-Arctic snow cover changes

We analyze snow cover extent (SCE) trends in the National Oceanic and Atmospheric Administration's (NOAA) northern hemisphere weekly satellite SCE data using the Mann-Kendall trend test and find that North American and Eurasian snow cover in the pan-Arctic have declined significantly in spring and summer over the period of satellite record beginning in the early 1970s. These trends are reproduced, both in trend direction and statistical significance, in reconstructions using the variable infiltration capacity (VIC) hydrological model. We find that spring and summer surface radiative and turbulent fluxes generated in VIC have strong correlations with satellite observations of SCE. We identify the role of surface energy fluxes and determine which is most responsible for the observed spring and summer SCE recession. We find that positive trends in surface net radiation (SNR) accompany most of the SCE trends, whereas modeled latent heat (LH) and sensible heat (SH) trends associated with warming on SCE mostly cancel each other, except for North America in spring, and to a lesser extent for Eurasia in summer. In spring over North America and summer in Eurasia, the SH contribution to the observed snow cover trends is substantial. The results indicate that ΔSNR is the primary energy source and ΔSH plays a secondary role in changes of SCE. Compared with ΔSNR and ΔSH, ΔLH has a minor influence on pan-Arctic snow cover changes

Increasing Fuel Loads, Fire Hazard, and Carbon Emissions from Fires in Central Siberia

The vast Angara region, with an area of 13.8 million ha, is located in the southern taiga of central Siberia, Russia. This is one of the most disturbed regions by both fire and logging in northern Asia. We have developed surface and ground fuel-load maps by integrating satellite and ground-based data with respect to the forest-growing conditions and the disturbance of the territory by anthropogenic and natural factors (fires and logging). We found that from 2001 to 2020, fuel loads increased by 8% in the study region, mainly due to a large amount of down woody debris at clearcuts and burned sites. The expansion of the disturbed areas in the Angara region resulted in an increase in natural fire hazards in spring and summer. Annual carbon emissions from fires varied from 0.06 to 6.18 Mt, with summer emissions accounting for more than 95% in extreme fire years and 31–68% in the years of low fire activity. While the trend in the increase in annual carbon emissions from fires is not statistically significant due to its high interannual variability and a large disturbance of the study area, there are significantly increasing trends in mean carbon emissions from fires per unit area (p p p p p < 0.025) forests. This indicates deeper burning and loss of legacy carbon that impacts on the carbon cycle resulting in climate feedback