What is the difference between atmosphere and hemisphere

Internal climate variability will play a major role in determining change on regional scales under global warming. In the extratropics, large-scale atmospheric circulation is responsible for much of observed regional climate variability, from seasonal to multidecadal timescales.

However, the extratropical circulation variability on multidecadal timescales is systematically weaker in coupled climate models. Here we show that projections of future extratropical climate from coupled model simulations significantly underestimate the projected uncertainty range originating from large-scale atmospheric circulation variability. Using observational datasets and large ensembles of coupled climate models, we produce synthetic ensemble projections constrained to have variability consistent with the large-scale atmospheric circulation in observations.

Compared to the raw model projections, the synthetic observationally-constrained projections exhibit an increased uncertainty in projected 21st century temperature and precipitation changes across much of the Northern extratropics. This increased uncertainty is also associated with an increase of the projected occurrence of future extreme seasons. Internal variability has a strong influence on decadal-to-multidecadal climate variability and trends, particularly on regional scales in the extratropics 1 , 2 , 3.

The dominance of internal variability explains why regional temperatures have exhibited markedly different trends on decadal timescales, despite persistent global warming due to increasing greenhouse gas concentrations since the pre-industrial period e. Eurasian winter cooling 4. The dominant source of internal variability for continental climate in the extratropics is large-scale atmospheric circulation. For example, the extratropical warming over land during the Northern Hemisphere winter over the later part of the twentieth century was enhanced substantially by anomalies in the large-scale atmospheric circulation and their associated impact on surface-air temperature 5 , 6 , 7.

Internal variability in the large-scale atmospheric circulation is expected to make a similarly large contribution to the climate we will experience in the future 8. Over the coming decades, trends in extratropical temperature and precipitation are expected to be dominated by internal variability, particularly over North America and Eurasia 2 , 9.

Therefore, to provide useful projections of extratropical climate over the twenty-first century, it is crucial that models accurately represent the contribution from the internal variability associated with large-scale atmospheric circulation Recent studies, however, have highlighted some disparities between the observed large-scale circulation variability and that seen in current climate models.

In particular, over the North Atlantic sector during winter, there have been significant multidecadal fluctuations in the leading mode of large-scale atmospheric circulation variability, the North Atlantic Oscillation 7 , 11 , 12 , and related behaviour in the strength of the North Atlantic jetstream 13 , Several studies have argued that the weak multidecadal atmospheric circulation variability reflects the apparently relatively weak response of the atmospheric circulation to variability in North Atlantic sea surface temperatures SSTs 15 , 16 , 17 , 18 , 19 , It has also been suggested that the response of stratospheric polar vortex to Atlantic SSTs and the subsequent influence on the extratropical large-scale circulation in the troposphere is poorly represented in climate models, which may contribute to the weak atmospheric circulation variability There is also substantial multidecadal variability in the large-scale circulation during the summer season in the North Atlantic sector, which has exhibited a clear influence on the variability of European summer climate 22 , 23 , 24 and—similarly to the winter season—seems to be too weakly represented in coupled climate models While there has been much attention given to the mechanisms of multidecadal variability in the atmospheric large-scale circulation, relatively little attention has been given to the implications for future projections The apparent disparities between the large-scale circulation variability in observations and models are discussed in the recent IPCC Sixth Assessment Report 27 Chapter 3 ; however, the implications for future projections are not clearly considered.

In this study, we use a novel method to produce climate projections using observationally constrained estimates of large-scale circulation variability. We begin our investigation by examining the multidecadal variability of sea-level pressure SLP in observational data sets.

SLP is a useful proxy for the atmospheric circulation because outside of the tropics the large-scale flow is effectively in geostrophic balance, such that SLP provides a direct measure of the near-surface winds. Large-scale SLP anomalies are also typically associated with wind anomalies higher in the troposphere 17 , 28 , so analysis of the SLP fields implicitly reflects variability throughout the troposphere.

For example, the only atmospheric observations that are used to constrain the 20th Century Reanalysis are surface pressure observations. Despite this, the 20th Century Reanalysis closely matches the variability of the upper-tropospheric extratropical circulation seen in more comprehensive reanalysis products that assimilate upper-atmosphere observations An advantage of analysing SLP here is that there is a long and extensive observational record 30 , with data from ship observations over the ocean and long station records over land Multidecadal variability is defined here as the standard deviation of the year running means, normalised by the interannual standard deviation.

The results are not sensitive to the length of the moving average timescale, and similar behaviour is found for decadal i. There are large areas with substantial multidecadal variability in winter SLP over the North Atlantic sector, significantly more than would be expected from a simple white-noise process as shown by the black contours in Fig. There are also areas with substantial multidecadal variability for the summer season.

The regions with high levels of summer multidecadal variability centred over Northern Europe and North America are particularly consistent across multiple observational data sets Supplementary Figs.

The multidecadal component is calculated using year running mean. The black hatching in e — h show where the multidecadal variance in the HadSLP2 observations for the respective seasons exceeds the 95th percentile of each of the ensemble distributions. The analysis of multidecadal SLP variability was repeated for a large ensemble of historical simulations from 54 different coupled climate models in the Coupled Model Intercomparison Project CMIP 5 and 6 archive 32 , Internal variability can be difficult to separate from the forced responses in the multi-model CMIP ensemble as different models have different forced responses that require large single-model ensembles to determine To examine the nature of the multidecadal SLP variability seen over the North Atlantic sector in observations, we performed an empirical orthogonal function EOF analysis using the regions shown in Fig.

Decomposing the interannual SLP data into the leading EOFs reveals that, in winter, the leading mode of variability EOF1 —often referred to as the North Atlantic Oscillation 36 —exhibits substantial multidecadal variability in all of the observational data sets Supplementary Fig. The contribution of the leading EOFs to the multidecadal SLP variability seen in the observations is assessed by replacing the principal component PC timeseries of the leading EOFs with random white-noise timeseries with the same standard deviation and repeating this process 10, times.

Similar to the winter season, the overall observed multidecadal summer SLP variability in the North Atlantic sector can be largely attributed to the first three EOFs Fig.

The discrepancies between the observed multidecadal SLP variability and that seen in climate models are substantial—but what are the implications for the climate projections made using these models? These projections are based on the MPI-GE climate model simulations, performed from to for various forcing scenarios The surrogate PC timeseries, therefore, tend to have more power on multidecadal timescales Supplementary Fig.

This process was repeated 10, times to produce a synthetic observationally constrained 10,member ensemble MPI-GE-obs hereafter. To include the influence of observational uncertainty, the surrogate PC timeseries were calculated from four different observational data sets, with each contributing equally to produce the 10, members in MPI-GE-obs.

A key assumption in producing MPI-GE-obs is that the future large-scale circulation variability will have the same characteristics as the large-scale circulation variability that we have observed in the past. This is highly uncertain, of course, but our aim here is to estimate what we might expect the variability of the large-scale atmospheric circulation to contribute in projections of future climate.

To some extent, this is justified by the modest forced changes in the large-scale circulation that we see in MPI-GE-raw but, usefully, this approach allows us to interpret any differences in the median projections as being driven by forced changes in large-scale circulation in MPI-GE-raw.

The forced changes are shown to be very modest in the results that follow; however, there is the potential that the forced future circulation changes are underestimated by the model 39 , To analyse the influence the observationally constrained large-scale circulation variability on future climate projections, we examine the changes in surface-air temperature and precipitation for the mid-century period — with respect to a present-day baseline period — in MPI-GE-raw and MPI-GE-obs.

There are, however, substantial differences in interquartile range i. Therefore, for many extratropical regions, the MPI-GE-raw ensemble underestimates the contribution of large-scale atmospheric circulation to the uncertainty in climate projections for the mid-twenty-first century. Distributions of the projected regional change of temperature and precipitation for the winter and summer seasons are summarised in Fig. For the Mediterranean region, there is more than a doubling of the likely range, with more substantial drying becoming much more likely in MPI-GE-obs.

The broadening of the distributions is also clear in the tails of the distributions, where mid-century changes in the winter temperature and precipitation that would be deemed highly unlikely are now well within the range of likely outcomes in the presence of observationally constrained large-scale circulation variability i.

Distributions of projected regional changes for the — mean from a — baseline period for: a Winter surface-air temperature, b winter precipitation, c summer surface-air temperature and d summer precipitation. The differences between the projections for changes in summer climate are relatively muted compared to the winter season. The reason for this is that the SLP EOFs exhibit a stronger relationship with temperature and precipitation anomalies in the summer season compared to the winter season Supplementary Figs.

Nonetheless, there are still significant increases in the interquartile range of the projected summertime precipitation changes over Northern Europe Figs. There are also significant changes in the distribution of projected temperatures over the East North America region Figs. From the analysis of the MPI-GE-obs projections, it is clear that, with large-scale circulation variability that is consistent with that seen in the observations, there is substantially more uncertainty in climate projections for many regions in the extratropics.

We have shown this for the mid-century period — but analysis of other year periods demonstrate similar increases in the spread of the distributions throughout the twenty-first century Supplementary Figs. Here we have presented results for year future climate periods because these are commonly considered in climate assessment reports 41 ; however, the uncertainty of the regional climate projections are similarly found to increase substantially in the Northern extratropics for , and year future periods Supplementary Figs.

Therefore, over any meaningful climatological averaging period in the future we expect there to be a substantial contribution from internal variability that is underestimated in regional climate model projections. As well as influencing the distribution of future regional climate change, it is also possible that the characteristics of the large-scale circulation variability considered here can influence extreme events on seasonal timescales.

However, the characteristics of when particular extreme seasons occur in individual model realisations can be quite different. The number of extreme seasons in a future climate period is then calculated in each ensemble member. The occurrence rate of extreme seasons for European winters are shown in Fig.

The occurrence of a greater number of extreme seasons within the year window is larger in MPI-GE-obs than in MPI-GE-raw in a number of instances, particularly in the tails of the distribution, whereas the occurrence of relatively few events tends to be higher in MPI-GE-raw.

Similarly, increases in the numbers of extremely wet winters over the mid-century period are found to be significantly more likely in MPI-GE-obs Fig. Another notable feature is that the occurrence of having a number of extremely dry Mediterranean winters over the mid-century period in the future is significantly higher in MPI-GE-obs Fig.

In the summer and the other extratropical regions, there are less clear differences in the occurrence of extreme seasons see Supplementary Information. The higher probability of a large number of extreme winter seasons occurring in a future period is related to the relatively large variability on multidecadal timescales in the MPI-GE-obs, which is absent in MPI-GE-raw.

An explanation for this is that the influence of low-frequency variability in the large-scale circulation can set a relatively high background anomaly over a year period, meaning that the year-to-year variability superimposed onto this can produce clusters of extreme seasons.

In MPI-GE-raw, however, there is relatively little low-frequency variability so the occurrence of future extreme seasons in a given year is largely independent of the surrounding years.

The panels show probability of exceeding a number of extreme seasons in the Northern Europe, Central Europe and Mediterranean regions for: a , e , i high temperatures; c , g , k low temperatures; b , f , j high precipitation; d , h , l low precipitation. The analysis presented here demonstrates that factoring the influence of the observed variability of the large-scale atmospheric circulation into future climate projections substantially increases the uncertainty arising from internal variability.

The current generation of coupled climate models, which are used to produce future climate projections, are therefore likely to underestimate the contribution of internal variability in the extratropics.

There are some significant differences in the projections of the MPI-GE-obs and MPI-GE-raw ensembles in the summer season around the North Atlantic sector but the influence of the observed large-scale atmospheric circulation on future projections is largest during the winter season, influencing most regions in the Northern extratropics. It is important to note, however, that the synthetic ensemble method used here likely misses some feedback mechanisms that will contribute to extratropical climate variability.

One example is the increase in the uncertainty in Mediterranean precipitation in the winter season i. Studies show that a wintertime precipitation deficit in the Mediterranean makes European heat waves more likely during the following summer 42 , 43 ; however, feedbacks relating to this are not captured in the synthetic MPI-GE-obs ensemble, in which winter and summer variability are effectively decoupled.

Another example of a missing feedback is that North Atlantic multidecadal SST variability has been shown to be driven in part by low-frequency variability of the wintertime large-scale atmospheric circulation 20 , 44 , 45 , 46 , 47 , In observational analysis, the multidecadal SST variability in the North Atlantic has been implicated for low-frequency climate variability during the summer season 23 , 49 , 50 , including the occurrence of heat waves 25 , 51 ; these possible feedbacks are also not captured in the synthetic MPI-GE-obs ensemble.

Each of these feedbacks would be expected to further increase the uncertainty in summer climate projections over the North Atlantic sector. The observed large-scale circulation variability that is used to produce the MPI-GE-obs projections is subject to substantial sampling uncertainty, particularly at the lower frequencies, due to the relatively short observational period.

While there is clearly substantial variability on timescales of decades and longer, the precise magnitude of this variability is uncertain. However, there is evidence from early instrumental and proxy reconstructions that the large-scale circulation over the North Atlantic sector exhibits distinct variability on multidecadal timescales 52 , 53 , 54 , in periods independent from the observational period considered here.

Therefore, while the precise degree to which internal variability is underestimated is fairly uncertain, it seems clear that future climate projections using the current coupled climate models significantly underestimate the internal variability of the large-scale atmospheric circulation.

For future twenty-first century periods, the underestimation of the uncertainty due to large-scale atmospheric circulation is comparable with the structural uncertainty in the forced response 55 , An example of where this underestimation could be important is the recent literature considering the differing impacts of 1.

Furthermore, the increased uncertainty also raises questions about the treatment of internal variability in regional model projections The EURO-CORDEX ensemble 59 , for example, use a relatively small subset of global coupled climate model simulations that, as has shown here, themselves underestimate the contribution of internal variability and this will be compounded in projections made using regional model ensembles.

The increased projection uncertainty may also be important to factor into future risk assessment and decision making exercises. We analyse data from the MPI-GE, which is a member ensemble comprising of a historical forcing simulation over the period — and the same members then follow the RCP 4. We use monthly averaged data from the historical forcing simulations over the common historical period — , with 82 ensemble members from CMIP5 and ensemble members from CMIP6.

HadSLP2 uses an optimal interpolation procedure using marine and land observations to reconstruct a gridded SLP field. Seasonal mean anomalies were only used where no more than one constituent month was missing. The observations and model data were compared e. The resulting PC timeseries from the model covering the period — were normalised over the common historical period — to be comparable with the observational data sets e.

Supplementary Fig. The projection approach described here was used to ensure that the PC timeseries correspond to the same patterns in all the observational and model data sets though tests calculating the EOF patterns from the different observational and model data sets leads to results that are qualitatively unchanged.

To generate the synthetic observationally constrained ensembles MPI-GE-obs , we first decompose the raw ensemble variables, X raw corresponding to the MPI-GE-raw ensemble in the main text , using the first three PC timeseries, as follows:. The decomposition is performed for all 99 members individually.

The synthetic observationally constrained ensemble variables, X obs , are produced as follows:. To generate the surrogate each timeseries, we use the method of Theiler et al.

First, the corresponding PC timeseries from one of the observational SLP data sets is selected and the discrete Fourier transform is computed. A random phase is added to each of the components, and the inverse Fourier transform is taken to return a random timeseries with similar spectral characteristics to the observed PC timeseries. It is important to note that the surrogate method acts to constrain the timeseries across all timescales.

Power spectra from the observational PC timeseries, surrogate PC timeseries i. The conclusions drawn from this ensemble are not qualitatively different, so here we only present results from MPI-GE-obs in the main text. One key assumption in the generation of MPI-GE-obs is that the temperature and precipitation anomalies associated with the leading EOFs are unchanged between the historical period and the future.

This is not entirely obvious, as it has been documented that interannual variability exhibits some robust changes in future climate model simulations 64 , which is also evident in some areas in the MPI-GE-raw ensemble Supplementary Fig. These values for Y n were used in the calculations of X obs in Eq. The results of the test shown in Supplementary Fig. From the full MPI-GE-obs ensemble, 99 members were selected without replacement; the required statistic e.

This subsampling was repeated 10, times to estimate the uncertainty associated with only having 99 ensemble members. The coupled model data and observational data used in the study are all publicly available data sets. Hawkins, E. The potential to narrow uncertainty in regional climate predictions. Article Google Scholar. Deser, C. Uncertainty in climate change projections: the role of internal variability. Marotzke, J. Forcing, feedback and internal variability in global temperature trends.

Nature , — McCusker, K. Twenty-five winters of unexpected eurasian cooling unlikely due to arctic sea-ice loss. Forced and internal components of winter air temperature trends over north america during the past 50 years: mechanisms and implications. Wallace, J. Simulated versus observed patterns of warming over the extratropical Northern Hemisphere continents during the cold season. Natl Acad. USA , — Iles, C. Role of the North Atlantic Oscillation in decadal temperature trends.

Maher, N. Quantifying the role of internal variability in the temperature we expect to observe in the coming decades. The role of the North Atlantic Oscillation in European climate projections. Shepherd, T. Atmospheric circulation as a source of uncertainty in climate change projections.

Kravtsov, S. Pronounced differences between observed and CMIP5-simulated multidecadal climate variability in the twentieth century.

Wang, X. Woollings, T. Twentieth century North Atlantic jet variability. A very hot summer sun may cause a sea breeze of up to 15 mph along the coast, felt in decreasing strength 20 to 25 miles inland. Since the sea breeze owes its existence to the enhanced heating of the land under the sun, it follows that at night, when the land cools faster than the sea, a land breeze may develop. In this case, it is air above the warmer surface water that is heated and rises, pulling in air from the cooler land surface.

Inland on clear nights when the surface looses considerable radiation, surface cooling serves to set up air movements wherever there are undulations of contour. As the air becomes colder, it contracts and sinks down as far as it can move, settling into hollows, drifting down slopes and blowing down mountain sides.

Large scale air movements of this nature are called Katabatic winds. On a global scale, the same principle of temperature difference operates to develop the major wind belts.

Large volumes of air rise over the equator where most solar radiation is directed, creating a demand for colder air from higher latitudes see Figure 3. This however, is an oversimplification of the cause of global weather.

The presence of large continental land masses and vast expanses of ocean introduce further complexities to the global air movements. These are looked at in lesson 9. Because the basic mechanism for raising air temperature occurs at ground level with the heating of the surface by the Sun, temperatures are generally higher near the Earth's surface than further away.

Nevertheless, local variations exist, caused by the slow mixing of air. Sometimes, air temperature decreases rapidly with altitude, sometimes more slowly. Occasionally, air temperature may even increase with altitude for a short distance. As discussed, when a packet of air near the earth's surface is heated, it rises, being lighter than the surrounding air. Whether or not this air packet continues to rise will depend upon how the temperature in the surrounding air changes with altitude.

As convection continues, air pressure begins to fall, and the air packet expands. Such expansion results in loss of heat and consequent fall in temperature. Similarly, when air descends the air compresses and its temperature rises.

The rate at which air on expansion cools is called the adiabatic lapse rate, and for dry air it is equal to 9. Adiabatic means that the air exchanges no heat with its surroundings, a condition very nearly true for rising and descending packets of air. If the rate at which the surrounding air temperature falls is less than the adiabatic lapse rate, a rising packet of heated air will cool faster, lose its buoyancy, and sink back to its original position.

In this case the atmosphere is said to be stable. If the rate at which the surrounding air temperature falls is greater than the adiabatic lapse rate, the packet of heated air will continue to rise.

The atmosphere in this circumstance is said to be unstable. When the air is saturated with water vapour, the processes are similar to those described above for dry air, but the adiabatic lapse rate is different. When saturated air rises and cools, condensation of water vapour begins, releasing latent heat.

Consequently the temperature in rising moist air falls less than it otherwise would. This approaches the value for the dry adiabatic lapse rate for much cooler air carrying little water vapour. More usually in the atmosphere, unsaturated air rises, cooling at the adiabatic lapse rate until it reaches its dew point. Thereafter, it behaves like saturated air. The moisture condensing out of the air becomes visible as cloud.

Assuming that land heats up faster than the sea, at what time will a sea breeze along the coast be strongest? In the figure below indicate the direction of katabatic air flow on an otherwise still night, and mark the region of coldest temperatures. Convection is the term commonly applied to vertical movement of air, whilst advection is used in the context of horizontal displacement of air.

This will occur when the incoming solar radiation balances the outgoing terrestrial radiation. At this time, the temperature of both the land and the sea reaches a maximum, but because the land heats up much faster than the sea, this is also the time of maximum temperature differences between land ad sea.

The warmer air over the sea then rises, pulling out the colder air over the land to generate a land breeze.