High-latitude Regions Of Northern Hemisphere Research Articles

Temperature sensitivity of vegetation phenology in spring in mid- to high-latitude regions of Northern Hemisphere during the recent three decades

Temperature sensitivity of vegetation phenology in spring in mid- to high-latitude regions of Northern Hemisphere during the recent three decades

Fast Warming Has Accelerated Snow Cover Loss during Spring and Summer across the Northern Hemisphere over the Past 52 Years (1967–2018)

With snow cover changing worldwide in several worrisome ways, it is imperative to determine both the variability in snow cover in greater detail and its relationship with ongoing climate change. Here, we used the satellite-based snow cover extent (SCE) dataset of National Oceanic and Atmospheric Administration (NOAA) to detect SCE variability and its linkages to climate over the 1967–2018 periods across the Northern Hemisphere (NH). Interannually, the time series of SCE across the NH reveal a substantial decline in both spring and summer (−0.54 and −0.71 million km2/decade, respectively), and this decreasing trend corresponded with rising spring and summer temperatures over high-latitude NH regions. Among the four seasons, the temperature rise over the NH was the highest in winter (0.39 °C/decade, p < 0.01). More precipitation in winter was closely related to an increase of winter SCE in mid-latitude areas of NH. Summer precipitation over the NH increased at a significant rate (1.1 mm/decade, p < 0.01), which likely contribute to the accelerated reduction of summer’s SCE across the NH. However, seasonal sensitivity of SCE to temperature changes differed between the Eurasian and North American continents. Thus, this study provides a better understanding of seasonal SCE variability and climatic changes that occurred at regional and hemispheric spatial scales in the past 52 years.

Mid-Pleistocene climate transition: Characteristic, mechanism and perspective

Milankovich theory suggested that orbitally induced summer insolation change at high-latitude region of Northern Hemisphere played a key role in driving the ice cycles during the Pleistocene. However, a significant shift of the Pleistocence climate from 41-ka to 100-ka cycles (namely mid-Pleistocene transition, MPT) cannot be simply attributed to the astronomical forcing due to insignificant insolation change induced by eccentricity forcing. Here we summarized multiple proxies generated from the ocean and land to address Pleistocene climate changes such as global ice volume, sea surface temperature, sea level, aridity of dust sources, and monsoon-related hydrological cycles. The temporal and spatial characteristics of the MPT phenomenon, in terms of the timing, duration, amplitudes and frequencies, were addressed using spectral and wavelet results. Most records reveal that the MPT was commenced as the onset of the ~100-ka cycles at ~1.2 Ma, and ended by the establishment of the dominant 100-ka cycles at ~0.7 Ma. However, some hydroclimatic proxies from the Mediterranean Sea, South China Sea and Chinese caves show a relatively strong 23-ka cycles during the middle to late Pleistocence. Such a difference may be attributable to varied sensitivity of proxy indicators to the insolation and glacial forcing. Globally, temperature and sea level signals that are affected by changing ice volume usually demonstrate a distinctive and consistent MPT. By contrast, regional hydroclimatic proxies at mid-to-low latitudes are likely dominated by the precession cycles (i.e. high-resolution, absolutely dated Chinese speleothem records). The MPT associated with the onset of ~100-ka cycles has inspired the paleocommunities to address its driving mechanism from both data and model perspectives. Several hypotheses have been proposed to explain the MPT in the past two decades, including nonlinear response to external forcing and complicated internal feedbacks. The nonlinear response to the astronomical forcing including eccentricity (100-ka cycle), interplay between obliquity (41-ka cycle) and precession (23- and 19-ka cycles) can produce the 100-ka ice-age cycles. However, model results suggest that the MPT and subsequent ″100 ka world″ are likely caused by changes in atmospheric CO2 concentration, bedrock exposure or ocean circulations. Taking these different hypotheses together, it was suggested that the solar insolation caused by changing Earth′s orbital parameters as external forcing, drives the glacial-interglacial climate cycles, whilst the nonlinear responses to atmospheric CO2 decrease and to different regolith boundary conditions as internal feedbacks have played key roles in triggering the climate transition and amplifying the glacial-interglacial oscillations. While most proxies from the land and ocean demonstrate a remarkable MPT from 41- to 100-ka cycles, regional high-resolution proxy records indicated that hydroclimatic changes at mid-to-low latitudes exhibit insignificant MPT and weak ~100-ka cycles after the MPT. In the future, more data assimilation should be focused on the hydroclimatic changes particularly in monsoon-affected regions to decipher the diverse responses in a regional scale. Meanwhile, simulations using high-resolution regional climate system models should be performed to quantify the responses of different climate parameters to external forcing and internal feedback. Through intensively data-model comparison, new insights into past climate variability and dynamics will permit a better projection of future climate change under the global warming context.

Tolerance adaptation and precipitation changes complicate latitudinal patterns of climate change impacts

Global patterns of biodiversity and comparisons between tropical and temperate ecosystems have pervaded ecology from its inception. However, the urgency in understanding these global patterns has been accentuated by the threat of rapid climate change. We apply an adaptive model of environmental tolerance evolution to global climate data and climate change model projections to examine the relative impacts of climate change on different regions of the globe. Our results project more adverse impacts of warming on tropical populations due to environmental tolerance adaptation to conditions of low interannual variability in temperature. When applied to present variability and future forecasts of precipitation data, the tolerance adaptation model found large reductions in fitness predicted for populations in high-latitude northern hemisphere regions, although some tropical regions had comparable reductions in fitness. We formulated an evolutionary regional climate change index (ERCCI) to additionally incorporate the predicted changes in the interannual variability of temperature and precipitation. Based on this index, we suggest that the magnitude of climate change impacts could be much more heterogeneous across latitude than previously thought. Specifically, tropical regions are likely to be just as affected as temperate regions and, in some regions under some circumstances, possibly more so.

Climate change hot‐spots

A Regional Climate Change Index (RCCI), is developed based on regional mean precipitation change, mean surface air temperature change, and change in precipitation and temperature interannual variability. The RCCI is a comparative index designed to identify the most responsive regions to climate change, or Hot‐Spots. The RCCI is calculated for 26 land regions from the latest set of climate change projections by 20 global climate models for the A1B, A2 and B1 IPCC emission scenarios. The Mediterranean and North Eastern European regions emerge as the primary Hot‐Spots, followed by high latitude northern hemisphere regions and by Central America, the most prominent tropical Hot‐Spot. The main African Hot‐Spots are Southern Equatorial Africa and the Sahara. Eastern North America is the prominent Hot‐Spot over the continental U.S. Different factors over different regions contribute to the magnitude of the RCCI, which is in fact greater than 0 for all regions.

Variability and trends of sub-continental scale surface climate in the twentieth century. Part II: AOGCM simulations

This work presents an analysis of simulated temperature and precipitation variability and trends throughout the twentieth century over 22 land regions of sub-continental scale in the HADCM3 and HADCM2 (two realizations) coupled models. Regional temperature biases in the HADCM3 and HADCM2 are mostly in the range of –5 K to +3 K for the seasonal averages and –3 K to +2 K for the annual average. Seasonal precipitation biases are mostly in the range of –50% to 75% of present day precipitation, with a tendency in both models to overpredict cold season precipitation. Except for cold season temperature in mid- and high-latitude Northern Hemisphere regions, the average climatology of the HADCM2 and HADCM3 is of comparable quality despite the lack of an ocean flux adjustment in the HADCM3. Both models show warming trends of magnitude in line with observations, although the observed inter-regional patterns of warming trend are not well reproduced. Measures of temperature and precipitation interannual to interdecadal variability in the models are in general agreement with observations except for Northern Hemisphere summer temperature variability, which is overestimated. The models somewhat underestimate the inter-decadal variations in interannual variability measures observed during the century and overestimate the range of anomalies. Both models tend to overpredict the occurrences of short persistences (1–3 years) and underpredict the occurrence and maximum length of long persistences (greater than three years), which is an indication of a deficiency in the simulation of long-lived anomaly regimes. Compared to observations, the models produce a higher magnitude of temporal anomaly correlation across regions and correlation between temperature and precipitation anomalies for a given region. This suggests that local processes that may be effective in decoupling the observed regional anomalies are not captured well. Overall, the variability measures in the HADCM2 and HADCM3 are of similar quality, indicating that the use of a flux correction in the HADCM2 does not strongly affect the regional variability characteristics of the model.