Abstract

New experiments are reported that extend previous studies of the internally generated variability found when the Hamburg LSG Ocean General Circulation Model is integrated under mixed boundary conditions. All model integrations have stochastic forcing added to the freshwater flux to excite the variability. It is demonstrated that the salinity anomalies that propagate around the meridional circulation of the Atlantic Ocean are merely signals emitted from the source of the variability in the Southern Ocean; they do not play an active role in its generation. It is the Southern Ocean flip–flop oscillator, as suggested by a previous study, that is the driving mechanism of the 320-yr period oscillations. A second mode of propagation is identified that may be related to the periodicity of the oscillations: westward propagation of upper-ocean salinity anomalies around the coast of Antarctica. It is shown that this mode is driven by the same density-upwelling wave motion as reported elsewhere in the literature.

The sensitivity of the simulated variability to changes in some of the model’s numerical and physical algorithms is investigated. The computationally expensive step of retriangularizing the matrix equation for the barotropic velocity can be done very infrequently without affecting the characteristics of the variability. Changing to a convective overturn parameterization that leaves fewer residual instabilities has a small effect on the variability, while changing to one that mixes, rather than interchanges, statically unstable water masses can reduce the magnitude of the variability by up to 70%. The latter change, however, is attributed entirely to the different freshwater flux forcing that the new parameterization implies. Using a more realistic haline and thermal coupling between sea ice and the ocean also leads to greatly reduced internal variability on the 320-yr timescale. Again, changes in surface fluxes implied by the alteration to the model are important, and these changes have implications for the flux adjustments necessary when the LSG model is coupled to an atmosphere model. The results presented here indicate considerable scope for reducing such flux adjustments.

Abstract

Observations of the time-mean thermohaline state of the world oceans are used to identify temperature gradients (and implied diffusive heat fluxes) both horizontally and on density surfaces. Two types of density surface are used: neutral and isopycnal, with the latter giving significantly different (and poorer) results. Along-neutral-surface (epineutral) heat fluxes are estimated to have a vertical component that is upward and strong in the upper 1500 m of the extratropical oceans, and weakly downward elsewhere. Horizontal heat diffusion has a dianeutral component with a similar pattern of strong and weak fluxes, although almost all fluxes are toward water of greater density.

Abstract

In a recent study it was found that the GFDL modular ocean model showed a small drift and very little variability when stochastically forced under mixed boundary conditions. This is in contrast to earlier studies that found very strong variability in a similar experiment with the Hamburg LSG ocean model. A number of possible reasons for such contrasting results, one of which is that the behavior of the two models is fundamentally different, have been discussed. In this comment, it is suggested that the main reason may be that the magnitude of the noise forcing used in the GFDL model was lower than that used in the Hamburg model.

Abstract

We simulate the response of Asian summer climate to Atlantic multidecadal oscillation (AMO)-like sea surface temperature (SST) anomalies using an intermediate-complexity general circulation model (IGCM4). Experiments are performed with seven individual AMO SST anomalies obtained from CMIP5/PMIP3 global climate models as well as their multimodel mean, globally and over the North Atlantic Ocean only, for both the positive and negative phases of the AMO. During the positive (warm) AMO phase, a Rossby wave train propagates eastward, causing a high pressure and warm and dry surface anomalies over eastern China and Japan. During the negative (cool) phase of the AMO, the midlatitude Rossby wave train is less robust, but the model does simulate a warm and dry South Asian monsoon, associated with the movement of the intertropical convergence zone in the tropical Atlantic. The circulation response and associated temperature and precipitation anomalies are sensitive to the choice of AMO SST anomaly pattern. A comparison between global SST and North Atlantic SST perturbation experiments indicates that East Asian climate anomalies are forced from the North Atlantic region, whereas South Asian climate anomalies are more strongly affected by the AMO-related SST anomalies outside the North Atlantic region. Experiments conducted with different amplitudes of negative and positive AMO anomalies show that the temperature response is linear with respect to SST anomaly but the precipitation response is nonlinear.

Abstract

Historical simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5) archive are used to calculate the zonal-mean change in seasonal land precipitation for the second half of the twentieth century in response to a range of external forcings, including anthropogenic and natural forcings combined (ALL), greenhouse gas forcing, anthropogenic aerosol forcing, anthropogenic forcings combined, and natural forcing. These simulated patterns of change are used as fingerprints in a detection and attribution study applied to four different gridded observational datasets of global land precipitation from 1951 to 2005. There are large differences in the spatial and temporal coverage in the observational datasets. Yet despite these differences, the zonal-mean patterns of change are mostly consistent except at latitudes where spatial coverage is limited. The results show some differences between datasets, but the influence of external forcings is robustly detected in March–May, December–February, and for annual changes for the three datasets more suitable for studying changes. For June–August and September–November, external forcing is only detected for the dataset that includes only long-term stations. Fingerprints for combinations of forcings that include the effect of greenhouse gases are similarly detectable to those for ALL forcings, suggesting that greenhouse gas influence drives the detectable features of the ALL forcing fingerprint. Fingerprints of only natural or only anthropogenic aerosol forcing are not detected. This, together with two-fingerprint results, suggests that at least some of the detected change in zonal land precipitation can be attributed to human influences.

Abstract

Pattern scaling is widely used to create climate change projections to investigate future impacts. We consider the performance of pattern scaling for emulating the HadGEM2-ES general circulation model (GCM) paying particular attention to “high end” warming scenarios and to different choices of GCM simulations used to diagnose the climate change patterns. We demonstrate that evaluating pattern-scaling projections by comparing them with GCM simulations containing unforced variability gives a significantly less favorable view of the actual performance of pattern scaling. Using a four-member initial-condition ensemble of HadGEM2-ES simulations, we infer that the root-mean-square errors of pattern-scaled monthly temperature changes over land are less than 0.25°C for global warming up to approximately 3.5°C. Some regional errors are larger than this and, for this GCM, there is a tendency for pattern scaling to underestimate warming over land. For warming above 3.5°C, the pattern-scaled projection errors grow but remain small relative to the climate change signal. We investigate whether patterns diagnosed by pooling GCM experiments from several scenarios are suitable for emulating the GCM under a high-end warming scenario. For global warming up to 3.5°C, pattern scaling using this pooled pattern closely emulates GCM simulations. For warming beyond 3.5°C, pattern-scaling performance is notably improved by using patterns diagnosed only from the high-forcing representative concentration pathway 8.5 (RCP8.5) scenario. Assessments of climate change impacts under high-end warming using pattern-scaling projections could be improved by using change patterns diagnosed from pooled scenarios for projections up to 3.5°C above preindustrial levels and patterns diagnosed from only strong forcing simulations for projecting beyond that. Similar findings are obtained for five other GCMs.

Abstract

Mediterranean-type climates are defined by temperate, wet winters, and hot or warm dry summers and exist at the western edges of five continents in locations determined by the geography of winter storm tracks and summer subtropical anticyclones. The climatology, variability, and long-term changes in winter precipitation in Mediterranean-type climates, and the mechanisms for model-projected near-term future change, are analyzed. Despite commonalities in terms of location in the context of planetary-scale dynamics, the causes of variability are distinct across the regions. Internal atmospheric variability is the dominant source of winter precipitation variability in all Mediterranean-type climate regions, but only in the Mediterranean is this clearly related to annular mode variability. Ocean forcing of variability is a notable influence only for California and Chile. As a consequence, potential predictability of winter precipitation variability in the regions is low. In all regions, the trend in winter precipitation since 1901 is similar to that which arises as a response to changes in external forcing in the models participating in phase 5 of the Coupled Model Intercomparison Project. All Mediterranean-type climate regions, except in North America, have dried and the models project further drying over coming decades. In the Northern Hemisphere, dynamical processes are responsible: development of a winter ridge over the Mediterranean that suppresses precipitation and of a trough west of the North American west coast that shifts the Pacific storm track equatorward. In the Southern Hemisphere, mixed dynamic–thermodynamic changes are important that place a minimum in vertically integrated water vapor change at the coast and enhance zonal dry advection into Mediterranean-type climate regions inland.

Abstract

Validation of the decadal to centennial timescale variability of coupled climate models is limited by the scarcity of long observational records. Proxy indicators of climate, such as tree rings, ice cores, etc., can be utilized for this purpose. This study presents a quantitative comparison of the variability of the third version of the Hadley Centre ocean–atmosphere coupled model with a network of temperature-sensitive tree-ring densities covering the northern high latitudes. The tree-ring density records are up to 600 years long, and temperature reconstructions based on two different methods of removing the bias due to changing tree age are used. The first is a standard method that may remove low-frequency variability on timescales of the order of the tree life span (i.e., multidecadal to century timescales). The second (age-band decomposition) maintains low-frequency variability by only comparing similar age tree rings at each site, thus avoiding the need to remove the age effect (but at the cost of greater uncertainty in the earlier years when fewer tree cores are available). The variability of the model control simulation, which represents only the internal variability of the climate system, agrees reasonably well with the tree-ring reconstructions using the standard method at the regional level, although the model may underestimate the variance of mean Northern Hemisphere land temperature by as much as a factor of 1.8 on all timescales if one takes account of the uncertainty in the tree-ring reconstructions. Agreement with the age-band decomposition tree-ring reconstructions is less good with the model underestimating the hemispheric variance by as much as a factor of 2.1 on all timescales and by as much as a factor of 3.0 on decadal to centennial timescales. Underestimation of the natural variability of climate by the model would be serious as it may lead to false detections of climate change or erroneously low uncertainty estimates in future climate predictions. However, it is shown that some of this underestimation may be due to the lack of natural climate forcing in the model control simulation due, for example, to solar variability and volcanic eruptions. The study suggests that further quantification of the uncertainties in the proxy data, and inclusion of natural climate forcings in the model simulations, are important steps in making comparisons of climate models with the proxy record over the last 1000 years.

Abstract

The physical mechanisms whereby the mean and transient circulation anomalies associated with the North Atlantic Oscillation (NAO) drive winter mean precipitation anomalies across the North Atlantic Ocean, Europe, and the Mediterranean Sea region are investigated using the European Centre for Medium-Range Weather Forecasts interim reanalysis. A moisture budget decomposition is used to identify the contribution of the anomalies in evaporation, the mean flow, storm tracks and the role of moisture convergence and advection. Over the eastern North Atlantic, Europe, and the Mediterranean, precipitation anomalies are primarily driven by the mean flow anomalies with, for a positive NAO, anomalous moist advection causing enhanced precipitation in the northern British Isles and Scandinavia and anomalous mean flow moisture divergence causing drying over continental Europe and the Mediterranean region. Transient eddy moisture fluxes work primarily to oppose the anomalies in precipitation minus evaporation generated by the mean flow, but shifts in storm-track location and intensity help to explain regional details of the precipitation anomaly pattern. The extreme seasonal precipitation anomalies that occurred during the two winters with the most positive (1988/89) and negative (2009/10) NAO indices are also explained by NAO-associated mean flow moisture convergence anomalies.

Abstract

The United Nations Framework Convention on Climate Change (UNFCCC) process agreed in Paris to limit global surface temperature rise to “well below 2°C above pre-industrial levels.” But what period is preindustrial? Somewhat remarkably, this is not defined within the UNFCCC’s many agreements and protocols. Nor is it defined in the IPCC’s Fifth Assessment Report (AR5) in the evaluation of when particular temperature levels might be reached because no robust definition of the period exists. Here we discuss the important factors to consider when defining a preindustrial period, based on estimates of historical radiative forcings and the availability of climate observations. There is no perfect period, but we suggest that 1720–1800 is the most suitable choice when discussing global temperature limits. We then estimate the change in global average temperature since preindustrial using a range of approaches based on observations, radiative forcings, global climate model simulations, and proxy evidence. Our assessment is that this preindustrial period was likely 0.55°–0.80°C cooler than 1986–2005 and that 2015 was likely the first year in which global average temperature was more than 1°C above preindustrial levels. We provide some recommendations for how this assessment might be improved in the future and suggest that reframing temperature limits with a modern baseline would be inherently less uncertain and more policy relevant.