The role of transient Arctic sea ice loss in the projected greenhouse gas–induced late-twentieth- to late-twenty-first-century climate change is investigated using the Geophysical Fluid Dynamics ...Laboratory’s Coupled Model version 3. Two sets of simulations have been conducted, one with representative concentration pathway (RCP) 8.5 radiative forcing and the second with RCP forcing but with Arctic sea ice nudged to its 1990 state. The difference between the two five-member sets indicates the influence of decreasing Arctic sea ice on the climate system. Within the Arctic, sea ice loss is found to be a primary driver of the surface temperature and precipitation changes. Arctic sea ice depletion also plays a dominant role in projected Atlantic meridional overturning circulation weakening and changes in North Atlantic extratropical sea surface temperature and salinity, especially in the first half century. The effect of present-day Arctic sea ice loss on Northern Hemisphere (NH) extratropical atmospheric circulation is small relative to internal variability and the future sea ice loss effect on atmospheric circulation is distinct from the projected anthropogenic change. Arctic sea ice loss warms NH extratropical continents and is an important contributor to global warming not only over high latitudes but also in the eastern United States. Last, the Arctic sea ice loss displaces the Pacific intertropical convergence zone (ITCZ) equatorward and induces a “mini-global warming” in the tropical upper troposphere.
Uncertainty in future climate change presents a key challenge for adaptation planning. In this study, uncertainty arising from internal climate variability is investigated using a new 40-member ...ensemble conducted with the National Center for Atmospheric Research Community Climate System Model Version 3 (CCSM3) under the SRES A1B greenhouse gas and ozone recovery forcing scenarios during 2000–2060. The contribution of intrinsic atmospheric variability to the total uncertainty is further examined using a 10,000-year control integration of the atmospheric model component of CCSM3 under fixed boundary conditions. The global climate response is characterized in terms of air temperature, precipitation, and sea level pressure during winter and summer. The dominant source of uncertainty in the simulated climate response at middle and high latitudes is internal atmospheric variability associated with the annular modes of circulation variability. Coupled ocean-atmosphere variability plays a dominant role in the tropics, with attendant effects at higher latitudes
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atmospheric teleconnections. Uncertainties in the forced response are generally larger for sea level pressure than precipitation, and smallest for air temperature. Accordingly, forced changes in air temperature can be detected earlier and with fewer ensemble members than those in atmospheric circulation and precipitation. Implications of the results for detection and attribution of observed climate change and for multi-model climate assessments are discussed. Internal variability is estimated to account for at least half of the inter-model spread in projected climate trends during 2005–2060 in the CMIP3 multi-model ensemble.
The role of ocean–atmosphere coupling in the zonal-mean climate response to projected late twenty-first-century Arctic sea ice loss is investigated using Community Climate System Model version 4 ...(CCSM4) at 1° spatial resolution. Parallel experiments with different ocean model configurations (full-depth, slab, and no interactive ocean) allow the roles of dynamical and thermodynamic ocean feedbacks to be isolated. In the absence of ocean coupling, the atmospheric response to Arctic sea ice loss is confined to north of 30°N, consisting of a weakening and equatorward shift of the westerlies accompanied by lower tropospheric warming and enhanced precipitation at high latitudes. With ocean feedbacks, the response expands to cover the whole globe and exhibits a high degree of equatorial symmetry: the entire troposphere warms, the global hydrological cycle strengthens, and the intertropical convergence zones shift equatorward. Ocean dynamics are fundamental to producing this equatorially symmetric pattern of response to Arctic sea ice loss. Finally, the absence of a poleward shift of the wintertime Northern Hemisphere westerlies in CCSM4’s response to greenhouse gas radiative forcing is shown to result from the competing effects of Arctic sea ice loss and greenhouse warming on the meridional temperature gradient in middle latitudes.
This study elucidates the physical mechanisms underlying internal and forced components of winter surface air temperature (SAT) trends over North America during the past 50 years (1963–2012) using a ...combined observational and modeling framework. The modeling framework consists of 30 simulations with the Community Earth System Model (CESM) at 1° latitude–longitude resolution, each of which is subject to an identical scenario of historical radiative forcing but starts from a slightly different atmospheric state. Hence, any spread within the ensemble results from unpredictable internal variability superimposed upon the forced climate change signal. Constructed atmospheric circulation analogs are used to estimate the dynamical contribution to forced and internal components of SAT trends: thermodynamic contributions are obtained as a residual. Internal circulation trends are estimated to account for approximately one-third of the observed wintertime warming trend over North America and more than half locally over parts of Canada and the United States. Removing the effects of internal atmospheric circulation variability narrows the spread of SAT trends within the CESM ensemble and brings the observed trends closer to the model’s radiatively forced response. In addition, removing internal dynamics approximately doubles the signal-to-noise ratio of the simulated SAT trends and substantially advances the “time of emergence” of the forced component of SAT anomalies. The methodological framework proposed here provides a general template for improving physical understanding and interpretation of observed and simulated climate trends worldwide and may help to reconcile the diversity of SAT trends across the models from phase 5 of the Coupled Model Intercomparison Project (CMIP5).
The impact of projected Arctic sea ice loss on the atmospheric circulation is investigated using the Whole Atmosphere Community Climate Model (WACCM), a model with a well-resolved stratosphere. Two ...160-yr simulations are conducted: one with surface boundary conditions fixed at late twentieth-century values and the other with identical conditions except for Arctic sea ice, which is prescribed at late twenty-first-century values. Their difference isolates the impact of future Arctic sea ice loss upon the atmosphere. The tropospheric circulation response to the imposed ice loss resembles the negative phase of the northern annular mode, with the largest amplitude in winter, while the less well-known stratospheric response transitions from a slight weakening of the polar vortex in winter to a strengthening of the vortex in spring. The lack of a significant winter stratospheric circulation response is shown to be a consequence of largely cancelling effects from sea ice loss in the Atlantic and Pacific sectors, which drive opposite-signed changes in upward wave propagation from the troposphere to the stratosphere. Identical experiments conducted with Community Atmosphere Model, version 4, WACCM’s low-top counterpart, show a weaker tropospheric response and a different stratospheric response compared to WACCM. An additional WACCM experiment in which the imposed ice loss is limited to August–November reveals that autumn ice loss weakens the stratospheric polar vortex in January, followed by a small but significant tropospheric response in late winter and early spring that resembles the negative phase of the North Atlantic Oscillation, with attendant surface climate impacts.
Marine ecosystems are undergoing rapid change at local and global scales. To understand these changes, including the relative roles of natural variability and anthropogenic effects, and to predict ...the future state of marine ecosystems requires quantitative understanding of the physics, biogeochemistry and ecology of oceanic systems at mechanistic levels. Central to this understanding is the role played by dominant patterns or “modes” of atmospheric and oceanic variability, which orchestrate coherent variations in climate over large regions with profound impacts on ecosystems. We review the spatial structure of extratropical climate variability over the Northern Hemisphere and, specifically, focus on modes of climate variability over the extratropical North Atlantic.
A leading pattern of weather and climate variability over the Northern Hemisphere is the North Atlantic Oscillation (NAO). The NAO refers to a redistribution of atmospheric mass between the Arctic and the subtropical Atlantic, and swings from one phase to another producing large changes in surface air temperature, winds, storminess and precipitation over the Atlantic as well as the adjacent continents. The NAO also affects the ocean through changes in heat content, gyre circulations, mixed layer depth, salinity, high latitude deep water formation and sea ice cover. Thus, indices of the NAO have become widely used to document and understand how this mode of variability alters the structure and functioning of marine ecosystems.
There is no unique way, however, to define the NAO. Several approaches are discussed including both linear (e.g., principal component analysis) and nonlinear (e.g., cluster analysis) techniques. The former, which have been most widely used, assume preferred atmospheric circulation states come in pairs, in which anomalies of opposite polarity have the same spatial structure. In contrast, nonlinear techniques search for recurrent patterns of a specific amplitude and sign. They reveal, for instance, spatial asymmetries between different phases of the NAO that are likely important for ecological studies.
It also follows that there is no universally accepted index to describe the temporal evolution of the NAO. Several of the most common measures are presented and compared. All reveal that there is no preferred time scale of variability for the NAO: large changes occur from one winter to the next and from one decade to the next. There is also a large amount of within-season variability in the patterns of atmospheric circulation of the North Atlantic, so that most winters cannot be characterized solely by a canonical NAO structure. A better understanding of how the NAO responds to external forcing, including sea surface temperature changes in the tropics, stratospheric influences, and increasing greenhouse gas concentrations, is crucial to the current debate on climate variability and change.
Time of emergence of anthropogenic climate change is a crucial metric in risk assessments surrounding future climate predictions. However, internal climate variability impairs the ability to make ...accurate statements about when climate change emerges from a background reference state. None of the existing efforts to explore uncertainties in time of emergence has explicitly explored the role of internal atmospheric circulation variability. Here a dynamical adjustment method based on constructed circulation analogs is used to provide new estimates of time of emergence of anthropogenic warming over North America and Europe from both a local and spatially aggregated perspective. After removing the effects of internal atmospheric circulation variability, the emergence of anthropogenic warming occurs on average two decades earlier in winter and one decade earlier in summer over North America and Europe. Dynamical adjustment increases the percentage of land area over which warming has emerged by about 30% and 15% in winter (10% and 5% in summer) over North America and Europe, respectively. Using a large ensemble of simulations with a climate model, evidence is provided that thermodynamic factors related to variations in snow cover, sea ice, and soil moisture are important drivers of the remaining uncertainty in time of emergence. Model biases in variability lead to an underestimation (13%–22% over North America and <5% over Europe) of the land fraction emerged by 2010 in summer, indicating that the forced warming signal emerges earlier in observations than suggested by models. The results herein illustrate opportunities for future detection and attribution studies to improve physical understanding by explicitly accounting for internal atmospheric circulation variability.
Recent observed climate trends result from a combination of external radiative forcing and internally generated variability. To better contextualize these trends and forecast future ones, it is ...necessary to properly model the spatiotemporal properties of the internal variability. Here, a statistical model is developed for terrestrial temperature and precipitation, and global sea level pressure, based upon monthly gridded observational datasets that span 1921–2014. The model is used to generate a synthetic ensemble, each member of which has a unique sequence of internal variability but with statistical properties similar to the observational record. This synthetic ensemble is combined with estimates of the externally forced response from climate models to produce an observational large ensemble (OBS-LE). The 1000 members of the OBS-LE display considerable diversity in their 50-yr regional climate trends, indicative of the importance of internal variability on multidecadal time scales. For example, unforced atmospheric circulation trends associated with the northern annular mode can induce winter temperature trends over Eurasia that are comparable in magnitude to the forced trend over the past 50 years. Similarly, the contribution of internal variability to winter precipitation trends is large across most of the globe, leading to substantial regional uncertainties in the amplitude and, in some cases, the sign of the 50-yr trend. The OBS-LE provides a real-world counterpart to initial-condition model ensembles. The approach could be expanded to using paleo-proxy data to simulate longer-term variability.
The authors investigate the atmospheric response to projected Arctic sea ice loss at the end of the twenty-first century using an atmospheric general circulation model (GCM) coupled to a land surface ...model. The response was obtained from two 60-yr integrations: one with a repeating seasonal cycle of specified sea ice conditions for the late twentieth century (1980–99) and one with that of sea ice conditions for the late twenty-first century (2080–99). In both integrations, a repeating seasonal cycle of SSTs for 1980–99 was prescribed to isolate the impact of projected future sea ice loss. Note that greenhouse gas concentrations remained fixed at 1980–99 levels in both sets of experiments. The twentieth- and twenty-first-century sea ice (and SST) conditions were obtained from ensemble mean integrations of a coupled GCM under historical forcing and Special Report on Emissions Scenarios (SRES) A1B scenario forcing, respectively.
The loss of Arctic sea ice is greatest in summer and fall, yet the response of the net surface energy budget over the Arctic Ocean is largest in winter. Air temperature and precipitation responses also maximize in winter, both over the Arctic Ocean and over the adjacent high-latitude continents. Snow depths increase over Siberia and northern Canada because of the enhanced winter precipitation. Atmospheric warming over the high-latitude continents is mainly confined to the boundary layer (below ∼850 hPa) and to regions with a strong low-level temperature inversion. Enhanced warm air advection by submonthly transient motions is the primary mechanism for the terrestrial warming. A significant large-scale atmospheric circulation response is found during winter, with a baroclinic (equivalent barotropic) vertical structure over the Arctic in November–December (January–March). This response resembles the negative phase of the North Atlantic Oscillation in February only. Comparison with the fully coupled model reveals that Arctic sea ice loss accounts for most of the seasonal, spatial, and vertical structure of the high-latitude warming response to greenhouse gas forcing at the end of the twenty-first century.
Marine ecosystems are undergoing rapid change at local and global scales. To understand these changes, including the relative roles of natural variability and anthropogenic effects, and to predict ...the future state of marine ecosystems requires quantitative understanding of the physics, biogeochemistry and ecology of oceanic systems at mechanistic levels. Central to this understanding is the role played by dominant patterns or “modes” of atmospheric and oceanic variability, which orchestrate coherent variations in climate over large regions with profound impacts on ecosystems. We review the spatial structure of extratropical climate variability over the Northern Hemisphere and, specifically, focus on modes of climate variability over the extratropical North Atlantic.
A leading pattern of weather and climate variability over the Northern Hemisphere is the North Atlantic Oscillation (NAO). The NAO refers to a redistribution of atmospheric mass between the Arctic and the subtropical Atlantic, and swings from one phase to another producing large changes in surface air temperature, winds, storminess and precipitation over the Atlantic as well as the adjacent continents. The NAO also affects the ocean through changes in heat content, gyre circulations, mixed layer depth, salinity, high latitude deep water formation and sea ice cover. Thus, indices of the NAO have become widely used to document and understand how this mode of variability alters the structure and functioning of marine ecosystems.
There is no unique way, however, to define the NAO. Several approaches are discussed including both linear (e.g., principal component analysis) and nonlinear (e.g., cluster analysis) techniques. The former, which have been most widely used, assume preferred atmospheric circulation states come in pairs, in which anomalies of opposite polarity have the same spatial structure. In contrast, nonlinear techniques search for recurrent patterns of a specific amplitude and sign. They reveal, for instance, spatial asymmetries between different phases of the NAO that are likely important for ecological studies.
It also follows that there is no universally accepted index to describe the temporal evolution of the NAO. Several of the most common measures are presented and compared. All reveal that there is no preferred time scale of variability for the NAO: large changes occur from one winter to the next and from one decade to the next. There is also a large amount of within-season variability in the patterns of atmospheric circulation of the North Atlantic, so that most winters cannot be characterized solely by a canonical NAO structure. A better understanding of how the NAO responds to external forcing, including sea surface temperature changes in the tropics, stratospheric influences, and increasing greenhouse gas concentrations, is crucial to the current debate on climate variability and change.
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