This study highlights the relative importance of internally generated versus externally forced climate trends over the next 50 yr (2010-60) at local and regional scales over North America in two global coupled model ensembles. Both ensembles contain large numbers of integrations (17 and 40): each of which is subject to identical anthropogenic radiative forcing (e.g., greenhouse gas increase) but begins from a slightly different initial atmospheric state. Thus, the diversity of projected climate trends within each model ensemble is due solely to intrinsic, unpredictable variability of the climate system. Both model ensembles show that natural climate variability superimposed upon forced climate change will result in a range of possible future trends for surface air temperature and precipitation over the next 50 yr. Precipitation trends are particularly subject to uncertainty as a result of internal variability, with signal-to-noise ratios less than 2. Intrinsic atmospheric circulation variability is mainly responsible for the spread in future climate trends, imparting regional coherence to the internally driven air temperature and precipitation trends. The results underscore the importance of conducting a large number of climate change projections with a given model, as each realization will contain a different superposition of unforced and forced trends. Such initial-condition ensembles are also needed to determine the anthropogenic climate response at local and regional scales and provide a new perspective on how to usefully compare climate change projections across models.

The Earth has warmed at an unprecedented pace in the decades of the 1980s and 1990s (IPCC in Climate change 2007: the scientific basis, Cambridge University Press, Cambridge, 2007). In Wu et al. (Proc Natl Acad Sci USA 104:14889-14894, 2007) we showed that the rapidity of the warming in the late twentieth century was a result of concurrence of a secular warming trend and the warming phase of a multidecadal (~65-year period) oscillatory variation and we estimated the contribution of the former to be about 0.08°C per decade since~1980. Here we demonstrate the robustness of those results and discuss their physical links, considering in particular the shape of the secular trend and the spatial patterns associated with the secular trend and the multidecadal variability. The shape of the secular trend and rather globally-uniform spatial pattern associated with it are both suggestive of a response to the buildup of well-mixed greenhouse gases.In contrast, the multidecadal variability tends to be concentrated over the extratropical Northern Hemisphere and particularly over the North Atlantic, suggestive of a possible link to low frequency variations in the strength of the thermohaline circulation. Depending upon the assumed importance of the contributions of ocean dynamics and the time-varying aerosol emissions to the observed trends in global-mean surface temperature, we estimate that up to one third of the late twentieth century warming could have been a consequence of natural variability.

The dominant patterns of Indian Summer Monsoon Rainfall (ISMR) and their relationships with the sea surface temperature and 850-hPa wind fields are examined using gridded datasets from 1900 on. The two leading empirical orthogonal functions (EOFs) of ISMR over India are used as basis functions for elucidating these relationships. EOF1 is highly correlated with all India rainfall and El Niño-Southern Oscillation indices. EOF2 involves rainfall anomalies of opposing polarity over the Gangetic Plain and peninsular India. The spatial pattern of the trends in ISMR from 1950 on shows drying over the Gangetic Plain projects onto EOF2, with an expansion coefficient that exhibits a pronounced trend during this period. EOF2 is coupled with the dominant pattern of sea surface temperature variability over the Indian Ocean sector, which involves in-phase fluctuations over the Arabian Sea, the Bay of Bengal, and the South China Sea, and it is correlated with the previous winter's El Niño-Southern Oscillation indices. The circulation anomalies observed in association with fluctuations in the time-varying indices of EOF1 and EOF2 both involve distortions of the low-level monsoon flow. EOF1 in its positive polarity represents a southward deflection of moist, westerly monsoon flow from the Arabian Sea across India, resulting in a smaller flux of moisture to the Himalayas. EOF2 in its positive polarity represents a weakening of the monsoon trough over northeastern India and the westerly monsoon flow across southern India, reminiscent of the circulation anomalies observed during break periods within the monsoon season.T he importance of Indian Summer Monsoon Rainfall (ISMR) for agricultural production, water availability, and food security is well-documented (1). Interannual monsoon variability strongly affects agricultural production, which accounts for about 22% of the Indian gross domestic product (2). Disruptions in the ISMR can lead to substantial losses in crop production that, in turn, may affect the food security of the large and growing population of India.July through September ISMR averaged over the entire Indian subcontinent is remarkably steady from one year to the next, with a coefficient of variation of only 9%. However, even these small variations have important consequences for food production. Rainfall over India as a whole is known to be negatively correlated with sea surface temperature (SST) anomalies over the equatorial eastern Pacific Ocean: it tends to be enhanced during the cold years and suppressed during the warm years of the El Niño-Southern Oscillation (ENSO) cycle (2-9). Rainfall during the monsoon season over India has also been linked with SST variability in the Indian Ocean: the Indian Ocean Dipole mode (10, 11) and a more general warming (cooling) of the tropical Indian Ocean during El Niño (La Niña) events through the socalled atmospheric bridge that persists into the following summer (12-14).Here, we identify a prominent pattern of year-to-year ISMR variability in which the anomalies exhibit a ...

A suite of the historical simulations run with the Intergovernmental Panel on Climate Change Fourth Assessment Report (IPCC AR4) models forced by greenhouse gases, aerosols, stratospheric ozone depletion, and volcanic eruptions and a second suite of simulations forced by increasing CO 2 concentrations alone are compared with observations for the reference interval 1965-2000. Surface air temperature trends are disaggregated by boreal cold (November-April) versus warm (May-October) seasons and by high latitude northern (N: 40°-90°N) versus southern (S: 60°S-40°N) domains. A dynamical adjustment is applied to remove the component of the coldseason surface air temperature trends (over land areas poleward of 40°N) that are attributable to changing atmospheric circulation patterns. The model simulations do not simulate the full extent of the wintertime warming over the high-latitude Northern Hemisphere continents during the later 20th century, much of which was dynamically induced. Expressed as fractions of the concurrent trend in global-mean sea surface temperature, the relative magnitude of the dynamically induced wintertime warming over domain N in the observations, the simulations with multiple forcings, and the runs forced by the buildup of greenhouse gases only is 7∶2∶1, and roughly comparable to the relative magnitude of the concurrent sea-level pressure trends. These results support the notion that the enhanced wintertime warming over high northern latitudes from 1965 to 2000 was mainly a reflection of unforced variability of the coupled climate system. Some of the simulations exhibit an enhancement of the warming along the Arctic coast, suggestive of exaggerated feedbacks.spatial patterns of warming | climate model diagnostics | dynamically-induced warming | polar amplification

The influence of atmospheric circulation changes reflected in spontaneously occurring sea level pressure (SLP) anomalies upon surface air temperature (SAT) variability and trends is investigated using partial least squares (PLS) regression, a statistical method that seeks to maximally explain covariance between a predictand time series or field and a predictor field. Applying PLS regression in any one of the three variants described in this study (pointwise, PC-wise, and fieldwise), the method yields a dynamical adjustment to the observed NH SAT field that accounts for approximately 50% of the variance in monthly mean, cold season data. It is shown that PLS regression provides a more parsimonious and statistically robust dynamical adjustment than an adjustment method based on the leading principal components of the extratropical SLP field. The usefulness of dynamical adjustment is demonstrated by applying it to the attribution of cold season SAT trends in two reference intervals: 1965–2000 and 1920–2011. The adjustment is shown to reconcile much of the spatial structure and seasonal differences in the observed SAT trends. The dynamically adjusted SAT fields obtained from this analysis provide datasets capable of being analyzed for residual variability and trends associated with thermodynamic and radiative processes.

Spontaneous, internally-generated variability of the climate system is pervasive. On the multidecadal time scale it dominates the variability of surface air temperature averaged over extratropical land areas as large as the contiguous United States, and it modulates the rate of rise of global mean temperature in response to the buildup of greenhouse gases. Unforced variability is one of the factors that imposes limitations on the degree of confidence that can be attached to assessments and predictions of human-induced climate change. This chapter summarizes results of some recent studies based on the analysis of large ensembles of numerical integrations run with a suite of different atmospheric initial conditions but with the same prescribed external forcing scenario. The future trajectory of the real climate system is, in some sense, like the trajectory of an individual member of such an ensemble. The diversity of the trends among the different ensemble members is a part of the irreducible uncertainty inherent in projections of future climate change. It is shown how statistical methods can be used to diagnose the causes of this diversity, most of which is in response to member-to-member diversity in the atmospheric circulation trends, as reflected in the associated patterns of the sea-level pressure trends. Interactions between the atmosphere, oceans, and land also contribute to the variability of surface air temperature trends on the multidecadal time scale, as discussed in Chapters XX and XX. It is argued that in the face of such large uncertainties in the attribution of climate change in the extratropics, more attention should be focused on climate change in the tropics, where the greenhouse warming signal stands out more clearly, and on the broader suite of environmental issues that impact food security and the viability of ecosystems.

Data from a dense urban meteorological network (UMN) are analyzed, revealing the spatial heterogeneity and temporal variability of the Twin Cities (Minneapolis–St. Paul, Minnesota) canopy-layer urban heat island (UHI). Data from individual sensors represent surface air temperature (SAT) across a variety of local climate zones within a 5000-km2 area and span the 3-yr period from 1 August 2011 to 1 August 2014. Irregularly spaced data are interpolated to a uniform 1 km × 1 km grid using two statistical methods: 1) kriging and 2) cokriging with impervious surface area data. The cokriged SAT field exhibits lower bias and lower RMSE than does the kriged SAT field when evaluated against an independent set of observations. Maps, time series, and statistics that are based on the cokriged field are presented to describe the spatial structure and magnitude of the Twin Cities metropolitan area (TCMA) UHI on hourly, daily, and seasonal time scales. The average diurnal variation of the TCMA UHI exhibits distinct seasonal modulation wherein the daily maximum occurs by night during summer and by day during winter. Daily variations in the UHI magnitude are linked to changes in weather patterns. Seasonal variations in the UHI magnitude are discussed in terms of land–atmosphere interactions. To the extent that they more fully resolve the spatial structure of the UHI, dense UMNs are advantageous relative to limited collections of existing urban meteorological observations. Dense UMNs are thus capable of providing valuable information for UHI monitoring and for implementing and evaluating UHI mitigation efforts.

[1] Application of the method of partial least squares (PLS) regression to geophysical data is illustrated with two cases: (1) finding sea level pressure patterns over the North Pacific associated with dynamically-induced winter-to-winter variations in snowpack in the Cascade mountains of western Washington state and (2) finding patterns of sea surface temperature over the tropical oceans that modulate Atlantic hurricane activity on a year-to-year basis. In both examples two robust patterns in the "predictor field" are identified that, in combination, account for over half the variance in the target time series. Citation: Smoliak, B. V., J. M. Wallace, M. T. Stoelinga, and T. P. Mitchell (2010), Application of partial least squares regression to the diagnosis of yearto-year variations in Pacific Northwest snowpack and Atlantic hurricanes, Geophys. Res. Lett., 37, L03801,