Analysing Arctic climate variability using historical upper-air and reanalysis data

Analysing Arctic climate variability using historical upper-air and reanalysis data

Andrea Nicole Grant

Research Plan

Institute for Atmospheric and Climate Science ETH Z¨urich

20 January 2006

1 Background

In order to understand the potential impacts of anthropogenically induced climate change, a better understanding of the natural variability of the climate system is required. This is especially the case in the Arctic, where both natural variability and trends are large. The con- sensus from numerous coupled ocean-atmosphere models is that the Arctic region will show enhanced warming from increased greenhouse gases (a so-called “polar amplification” ef- fect) (R¨ais¨anen, 2001), with warming of up to three times the global mean warming (Holland and Bitz, 2003). This prediction has not been unanimously confirmed in analyses of 20th century data. Some have seen observational evidence of enhancement of global warming in the Arctic (Moritz et al., 2002; Johannessen et al., 2004; Overpeck et al., 1997), and paleocli- mate records show that, over the ice ages, tropical surface air temperature (SAT) changed by only a few degrees while polar SAT changed by 10

C or more (Hartmann, 1994). However, Polyakov et al. (2002) have found the recent warming to be part of a low frequency oscilla- tion with trends in Arctic SAT approximately equal to Northern Hemisphere SAT trends. This work will focus on natural variability of the Arctic climate, specifically in the 20th century.

Polar regions play critical role in driving some of the primary feedback processes in the ocean-atmosphere system, e.g., those involving planetary albedo, thermal inertia and the sur- face heat flux, as well as playing an important role in the thermohaline circulation (THC) of the oceans. The state of the Arctic has a considerable influence on global climate and vice versa. Recent evidence suggests increasing melting of Arctic sea ice, with the likely loss of permanent (i.e. summer) ice in the coming century (Overpeck et al., 2005; Meier et al., 2005). Climate in the Arctic region is influenced by many factors, for example: large scale atmospheric and oceanic circulation patterns such as the Arctic Oscillation and the Merid- ional Overturning Circulation, sea ice and cloud cover which both impact the surface energy budget, regional variability in the ocean-atmosphere heat flux, latitude limited insolation, and possible stratospheric influences.

The surface energy budget is impacted by both radiative and non-radiative energy terms.

Incoming radiative energy consists of shortwave (solar) and longwave (emitted from the mid- dle and upper atmosphere), while terrestrial blackbody radiation comprises the outgoing en- ergy. Shortwave radiation obviously varies significantly with season. Incoming longwave radiation is dependent on a number of factors, including cloud cover and the thermal struc- ture of the atmosphere. Cloud observations can be problematic, as they are frequently under- estimated by manual observers at night (Hahn et al., 1995), and satellite sensing algorithms have difficulty distinguishing between clouds and snow and ice surfaces (Serreze and Barry, 2005). Furthermore, the parameterization of cloud cover in GCMs is problematic, especially in polar regions (Serreze and Barry, 2005). Non radiative factors in the surface energy budget include sensible and latent heat, with melting of snow and ice playing a large part. Conduc- tive energy transfer is also important and can vary dramatically due to the highly insulating nature of snow.

A dramatic warming of the Arctic occurred between the 1920s and 1940s, with the av-

erage temperature reaching 1.7

C above normal (Polyakov et al., 2003b). The cause of this

warming and subsequent cooling is not well understood, although it is generally believed to

be evidence of natural variability. Several mechanisms have been suggested, such as:

• deepening of the Icelandic Low increased westerly and southwesterly winds and thus increased warm air advection (Rogers, 1985; Fu et al., 1999)

• a local (non-NAO related) pressure gradient between Spitsbergen and the northernmost Norwegian coast caused increased flow of warm water into the Barents Sea, decreasing sea ice and increasing surface heat flux and SAT. Much of the variability in Arctic temperature can be linked to temperatures in this region (Bengtsson et al., 2004)

• larger scale, NAO-enhanced THC (Delworth and Knutson, 2000), natural low fre- quency oscillation (LFO) of the North Atlantic and THC (Polyakov et al., 2003b), or LFO of the north Atlantic not related to the NAO (Johannessen et al., 2004)

• a more local, natural LFO of SAT which also includes the warming in recent decades (Polyakov et al., 2003b) although Johannessen et al. (2004) argued against this, noting that the early twentieth century warming and recent warming have distinctly different spatial patterns

• a predominance of an SLP dipole (the ‘third SLP EOF’) in the early 1930s, which has also been seen in spring of recent years, although no LFO was suggested (Overland and Wang, 2005)

• a period of unusually low volcanic activity (Overpeck et al., 1997) although Bengtsson et al. (2004) found no evidence of such a link and Delworth and Knutson (2000) were able to simulate the warming without including the effects of volcanic or solar forcing

• solar irradiance changes (Soon, 2005; Overpeck et al., 1997), possibly involving the stratosphere, although reconstructions of solar forcing are somewhat unreliably linked to SAT trends (Bengtsson et al., 2004; Delworth and Knutson, 2000; Johannessen et al., 2004)

Previous analyses have been limited to surface atmospheric data as these are the only data currently available for the first half of the 20th century. Analysis of this period using historical upper air and reanalysis would add significantly to our understanding of this sensitive region and provide a more complete picture of the early twentieth century warming.

2 Objective

The primary objective of this PhD is to improve our understanding of the cause of the dra-

matic warming and subsequent cooling of the Arctic in the first half of the 20th century. This

will be accomplished through the analysis of large scale climate variability and dynamical

features such as the positions of the jet streams, planetary wave structure, and the strength of

the polar vortex. Upper air data provides significant insight into large scale circulation as well

as regional anomalies and transient features. The primary dataset is newly available histori- cal upper air data and reanalysis and is discussed in detail below. With this new dataset, the surface based hypotheses detailed above regarding the ETCW will be tested from an upper level perspective.

Some specific research questions are

1. Did the warming seen in Arctic surface temperatures extend into upper levels? Was there cooling in the stratosphere?

2. What was the strength of the polar vortex during this time?

3. Was there an increase in cyclonicity in the region of the Barents and Kara Seas which led to a greater influx of warm Atlantic water (Bengtsson et al., 2004) (i.e., mechanism 2)?

4. Was the structure of the surface/low level inversion different during this time?

5. Does the atmosphere provide evidence for changes in surface (latent and sensible) heat fluxes?

3 Data and Methods

This project will utilize a variety of datasets and derived products. Surface data will include land and sea surface temperatures from individual stations and in gridded form, along with sea ice data. Upper air data will include original radiosonde and pilot balloon data as well as model and assimilation products such as the new NOAA CIRES-CDC reanalysis product extending back to 1900 (Compo et al., 2005; Whitaker et al., 2004) and reconstructions of upper level gridded fields from the soundings using the technique described in Br¨onnimann and Luterbacher (2004) which will be carried out by other members of this research group.

Coupled ocean-atmosphere model simulations will also be used to investigate similar warm periods. Other possible datasets include an alternative reanalysis based on assimilation of monthly means in collaboration with van der Schrier and Barkmeijer (2005), previous recon- structions back to 1939 (Br¨onnimann and Luterbacher, 2004), historical ozone data, and a reconstruction of sea ice created by this research group.

3.1 Surface data

Surface air temperatures (SAT) are available at individual stations in datasets from Polyakov et al. (2002) and the Global Historical Climatology Network (Peterson and Vose, 1997).

Gridded SAT are available from the Climate Research Unit temperature data (CRUTEM2)

(Jones and Moberg, 2003). Gridded sea ice and sea surface temperatures (SST) are available

from the Hadley Centre (HadISST) (Rayner et al., 2003). The combined SAT and SST are

available as the HadCRUT2 dataset. Surface pressure is also available from the Hadley Centre

(HadSLP) (Basnett and Parker, 1997).

3.2 Historical data

Little upper air data is available in digital form for the period of the early twentieth century warming. Paper archives are currently being digitised using the methods described below.

Upper air soundings from 1948 to 1957 are already available in digital form (Durre et al., in press; Eskridge et al., 1995). However, many instrumentation problems are still evident in this early record (Lanzante et al., 2003). These early data will be validated using the statistical methods mentioned below, as they are useful for understanding the significant cooling in the Arctic which followed the 1920s and 1930s warming.

Historical upper air data is digitised using a combination of voice recognition and OCR and is controlled for transcription errors. Little metadata exists, so quality assurance of each series relies largely on statistical methods such as:

• Reconstructing a reference series based on surface information

• Tests for bias and acceptable precision (Br¨onnimann, 2003)

• Identify and correct lag and radiation errors (Br¨onnimann, 2003)

• Comparison with neighbouring stations if available

• Comparison with total ozone (for stratospheric levels)

• Homogeneity tests (Alexandersson and Moberg, 1997; Lanzante, 1996)

3.3 Reanalysis

Current reanalysis (Kistler et al., 2001) relies on assimilating surface, upper air, and satellite data into a fixed, state of the art numerical weather prediction model. This provides gridded 6 hourly output, currently extending back to 1948, which has provided extensive insight into the atmosphere. Before 1948, upper air data has not been available in digital form, therefore it was not seen as feasible to extend reanalysis further back. Improvements in assimilation algorithms, however, have made it possible to create a usable historical reanalysis extending back to 1900 based entirely on surface pressure (Compo et al., 2005; Whitaker et al., 2004).

In a preliminary study, Compo et al. (2005) found that “for the Northern Hemisphere winter, such a reanalysis using the Ensemble Filter can be expected to be of high quality, as measured by analysis errors that are substantially less than the climatological standard deviation for all dynamical variables. We expect the analysis error to be on the order of the modern NWP 1 to 2 day forecast error in the lower troposphere and the 2 to 3 day forecast error in the middle and upper troposphere.” This is sufficient for most climatological applications, however the output will require validation with historical upper air data prior to use.

Much historical upper air data is currently being digitised. However, the variety of types

(aircraft, kites, pilot balloons, early soundings) and formats as well as spatial and temporal

irregularities increase the complexity of assimilating this data. Initially, it will be assimilated

into this historical reanalysis in trial periods in order to quantitatively measure the resulting

improvement in output. If it is substantial, the reanalysis may be rerun using all available historical upper air data. Regardless, even surface based reanalysis will provide useful data.

3.4 Coupled Climate Models

The Community Climate System Model 3 (CCSM3) from NCAR is a fully coupled ocean- atmosphere-sea ice model (Collins et al., submitted). Output from both control runs and runs forced with SSTs will be analysed for periods with spatially similar warming in the Arctic in order to gain a better understanding of the atmospheric and oceanic dynamics that accompany such a regional warming. Output from a 540 year control run based on 1990 conditions will be used.

3.5 Reconstructions and group member datasets

Upper level gridded fields will be reconstructed by group member Thomas Griesser directly from the newly digitised upper air data, following the technique described in (Br¨onnimann and Luterbacher, 2004) and will also be available for analysis.

Model output will also be available from the chemistry climate model SOCOL (Egorova et al., 2004) which will be forced with time varying boundary conditions representing the 20th century by group member Andreas Fischer.

Historical records of sea ice extent and thickness are incomplete. The HadISST dataset contains sea ice concentrations for each gridbox, based on the Walsh (1978) data. However, prior to 1948 much of this data is climatological due to the sparsity of observations, especially in winter. Polyakov et al. (2003a) recently published a regional sea ice dataset of August ice extent in the Kara, Laptev, East Siberian, and Chukchi seas while Johannessen et al. (2004) published data from the Greenland and Barents Seas as well as the Siberian sector. Historical ice charts from ship log books have also been digitised (Brinck Lyning et al., 2003). However, most data is from a relatively limited geographical area (the Atlantic sector of the Arctic) and almost exclusively covers only summer months. Therefore a reconstruction of year round sea ice based on these data and a suitable model is being considered.

3.6 Other possibilities

Additional datasets which may be utilised include total ozone at Tromsø (70

N, 1935 to

present) and Spitsbergen (80

N, 1950 to present) for insight into stratospheric dynamics, as

well as reconstructed upper level fields from 1939 to 1944 (Br¨onnimann and Luterbacher,

2004). van der Schrier and Barkmeijer (2005) discuss a reconstruction technique which in-

volves assimilating monthly means into a coupled ocean atmosphere model in such a way

that the monthly means are consistent with observations, while “leaving the atmosphere free

to respond in a dynamically consistent way to any changes in climatic conditions.” Poten-

tial collaboration and utilisation of this technique would provide an alternative dataset for

analysis.

4 Analysis

Following the digitisation and validation of the historical data and reanalysis, analysis of the data will fall into three parts. First, a case study of the warmest winters will be made.

Second, statistical analysis of the reanalysis will reveal key aspects of variability, and third, the overall warming and cooling periods will be analysed in both the reanalysis and similar periods in model output. Three papers will be written over the course of the PhD: 1) pre-1958 radiosonde data, 2) case study of the warmest winter, and 3) causes of the Arctic warming.

4.1 Case study

A case study will be made on 1937 (and possibly 1944), the warmest winter(s) in the Arctic.

Anomalous states will be looked for in surface and upper air parameters such as sea level pressure, low level temperature inversions, sea ice extent, and cyclonicity, as well as the stan- dard indices such as the Arctic Osciallation/North Atlantic Oscillation and the Pacific/North America pattern.

4.2 Statistical Analysis

Statistical analysis of the data will reveal characteristic indicators which best capture the anomalous warming of the early 20th century. Results of the warmest winters case study will also aid in identifying meaningful metrics. Variability of key features will be quantified.

4.3 Model-observation comparison

Periods with similarly warm arctic SAT will be identified in CCSM model output. These will be compared with the reanalysis data with regard to underlying dynamical features. A complete picture of the extent and causes of the Arctic warming will be collected, as well as the context of natural variability.

4.4 First Results

Preliminary work with the data is underway.

• Soundings from Ilmala, Rovaniemi, and ¨ A¨anislinna Finland from 1942 to 1947 were verified using the techniques described above.

• The newly released Integrated Global Radiosonde Archive (IGRA) dataset contains

around 50 stations with data prior to 1948. During validation of these stations using

the techniques described above, it was discovered that there was an error in the units at

stations from the Former Soviet Union which had not been caught with the pre-release

quality assurance algorithms.

• 1937 and 1944 were identified as the warmest winters in the Arctic as a consensus result from several station and gridded temperature datasets using a number of commonly used definitions of the Arctic itself (Przybylak, 2003). Several individual winters which match the spatial warming pattern of 1937 have been identified in the 540 year CCSM control run.

4.5 Timeline

Date Component

Jun 2005 Begin PhD

Jul 2005 Validation of 1942–1947 Finnish radiosonde data Aug 2005 Familiarisation with and extraction of IGRA data

Establishment of group database with surface and upper air data

Aug 2005 - Jun 2006 Historical data entry and validation

Sep - Oct 2005 Background reading, familiarisation with CCSM3

Winter 2005–2006 Preliminary model analysis, comparison with surface data Verification of compiled pre-1957 radiosonde data

Spring 2006 EGU (report on validation of pre-1957 radiosonde data) Summer 2006 Verification of reanalysis data, paper together with NOAA Fall 2006 First paper on pre-1957 radiosonde data

Analysis of reanalysis, further model runs (if necessary) Spring 2007 Second paper, a case study of the warmest winter, EGU 2007 Comparison of all datasets and statistical analysis 2008 Third paper on causes of Arctic Warming, EGU June 2008 Completion of thesis

5 Collaborations

This project will involve local and international collaborations.

At ETH

• Other group members, Andreas Fischer and Thomas Griesser, through the exchange of

datasets

In Europe

• P. Thorne of the Hadley Centre through the exchange of historical upper air data

• A. Nagurny and A. Sterin in Russia through the “East West collaboration” and ex- change of historical upper air data

• G. van der Schrier and J. Barkmeijer for assimilating monthly means into a climate model

International

• G. Combo, J. Whicker, and P. Stagestruck during the assimilation into and validation of the NOAA CIRES-CDC reanalysis

• R. Jenny, J. Comedic of NCAR through the exchange of historical upper air data

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6 Acceptance of Research Plan

PhD Supervisor

Prof. Dr. Stefan Br¨onnimann

PhD Student

Andrea Grant