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Global Warming 1-2100

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Joseph Nowarski at Nowarski Engineering
Joseph Nowarski
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Abstract and Figures

This work combines global warming data from various publications and datasets, creating a new dataset covering a very long period-from the year 1 to 2100. The dataset created in this work separates the actual records for the 1-2024 period from the forecast for the 2020-2100 period. The work includes separate sets for land+ocean (GW), land only (GWL), and ocean only (GWO). The online dataset is available on the site nowagreen.com.
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Global Warming 1-2100
Joseph Nowarski, M.Sc., ME – Energy Conservation Expert
Version 1.1.1, 16 March 2025
DOI:10.5281/zenodo.15034763
all versions DOI:10.5281/zenodo.15034762
Abstract
This work combines global warming data from various publications and datasets,
creating a new dataset covering a very long period - from the year 1 to 2100.
The dataset created in this work separates the actual records for the 1-2024 period
from the forecast for the 2020-2100 period.
The work includes separate sets for land+ocean (GW), land only (GWL), and
ocean only (GWO).
The online dataset is available on the site nowagreen.com.
Global Warming 1-2100 – Joseph Nowarski
2/11
Glossary
Amp amplitude of data around trendline
Ave Average
BAU Business as Usual
BL baseline of surface temperature change (GW),
pre-industrial = 1850-1900
CF Conversion Factor across baselines, °C
CF(pBL) Conversion Factor to 1850-1900 preindustrial baseline, °C
GW Global Warming, global surface temperature of land+ocean, °C
above the 1850-1900 baseline
GWL global surface temperature over land only, °C above the 1850-1900
baseline
GWO global surface temperature over the ocean only, °C above the 1850-
1900 baseline
miniAmp dataset with reduced amplitude
Paleo Global Warming dataset based on paleoclimate records in the last
2,000 years [1] [2]
pBL global warming data above 1850-1900 preindustrial period baseline
Ref reference
Thermo thermal measurements for determination of global warming using
thermometers and satellites (after 1750)
TL trendline
Global Warming 1-2100 – Joseph Nowarski
3/11
Global Warming Baseline and Units
All results of calculations in this work are in °C above the preindustrial 1850-1900
baseline (pBL).
Global Warming Land+Ocean Data Sources for the 1-2024 Period
Table 1 - Data sources for the 1-2024 period land+ocean [9]
Ref ID 1-1850 1850-1880 1880-1950 1950-2000 2000-2014 2014-2024
Paleo [1] [2] DB11
Thermo [1] [2] DB12
NASA [3] [4] DB13
NOAA [5] DB14
LBL [6] [7] DB15
IPCC [8] DB16
Ave [9] DB17
Table 2 - Datasets for the 1-2024 period land+ocean [9]
Name Paleo Thermo NASA NOAA Berkley Earth
IPCC Ave
ID DB11 DB12 DB13 DB14 DB15 DB16 DB17
Reference [1] [2] [1] [2] [3] [4] [5] [6] [7] [8] [9] [10]
Units °C °C °C °C °C °C °C
Records annual annual annual annual annual annual annual
From 1 1850 1880 1850 1850 1950 1
To 2000 2017 2024 2024 2024 2044 2024
Years 2,000 168 145 175 175 95 2024
Baseline (BL) 1961-1990 1961-1990 1951-1980 1901-2000 1951-1980 1850-1900 1850-1900
BL years 30 30 30 100 30 51 51
Ave in BL -0.0008 0.0000 -0.0003 0.0002 0.0174 N/A 0.0063
Decimal places 4 4 2 2 3 6 3
CF(pBL) +0.3794 +0.3536 +0.2807 +0.1704 +0.3062 N/A N/A
The dataset applied in this work is DB17 which is the average of all other datasets
converted to the preindustrial baseline in publications [9] [10].
Global Warming 1-2100 – Joseph Nowarski
4/11
Chart 1 - All datasets - Global Warming (Land+Ocean) 1-2024, °C above pBL
-0.5
0.0
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Paleo Thermo NASA NOAA LBL IPCC Ave
Global Warming over Land Data Sources for the 1750-2024 Period
Table 3 - GWL (land only) data sources for the 1750-2024 period [9]
Ref ID 1750-1850 1850-1880 1880-2024
NASA [3] [4] DB21
NOAA [5] DB22
LBL [6] [7] DB23
LBL miniAmp [6] [7] [9] [10] DB24
Ave [9] [10] DB25
Table 4 - GWL (land only) datasets for the 1750-2024 period [9]
Name NASA NOAA Berkley Earth miniAmp Ave
ID DB21 DB22 DB23 DB24 DB25
Reference [3] [4] [5] [6] [7] [6] [7] [9] [10] [9] [10]
Units °C °C °C °C °C
Records annual annual annual annual annual
From 1880 1850 1750 1750 1750
To 2024 2024 2024 2024 2024
Years 145 175 275 275 275
Baseline (BL) 1951-1980 1901-2000 1951-1980 1850-1900 1850-1900
BL years 30 100 30 51 51
Ave in BL 0.0003 0.0000 0.0000 -0.0366 -0.0218
Decimal places 2 2 3 3 3
CF(pBL) 0.4428 +0.4512 +0.4540 N/A N/A
Global Warming 1-2100 – Joseph Nowarski
5/11
The dataset applied in this work is DB25 which is the average of DB21, DB22 and
DB24 converted to the preindustrial baseline in publications [9] [10].
Chart 2 - GWL (land only) all datasets 1750-2024, °C above pBL
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
1750
1760
1770
1780
1790
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1830
1840
1850
1860
1870
1880
1890
1900
1910
1920
1930
1940
1950
1960
1970
1980
1990
2000
2010
2020
NASA NOAA LBL miniAmp Ave
There is very small difference between the datasets, which cannot be
distinguished on the above chart (except the 1850-1860 period)
Global Warming 1-2100 – Joseph Nowarski
6/11
Global Warming over Ocean Data Sources for the 1750-2024 Period
Table 5 - GWO (ocean only) data sources for the 1750-2024 period [9]
Ref ID 1750-1850 1850-1880 1880-2024
NASA [3] [4] DB31
NOAA [5] DB32
DB33 DB1+DB25 DB33
Ave [9] [10] DB34
Table 6 - GWO (ocean only) datasets for the 1750-2024 period [9]
Name NASA NOAA DB33 Ave
ID DB31 DB32 DB33 DB34
Reference [3] [4] [5] [9] [9]
Units °C °C °C °C
Records annual annual annual annual
From 1880 1850 1750 1750
To 2024 2024 1850 2024
Years 145 175 101 275
Baseline (BL) 1951-1980 1901-2000 1850-1900 1850-1900
BL years 30 100 51 51
Ave in BL 0.0007 0.0000 N/A 0.0025
Decimal places 2 2 3 3
CF(pBL) +0.0981 +0.0441 N/A N/A
The dataset applied in this work is DB34 which is the average of all other datasets
converted to the preindustrial baseline in publications [9] [10].
Chart 3 - GWO (ocean only) all datasets 1750-2024, °C above pBL
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1750
1760
1770
1780
1790
1800
1810
1820
1830
1840
1850
1860
1870
1880
1890
1900
1910
1920
1930
1940
1950
1960
1970
1980
1990
2000
2010
2020
NASA NOAA DB33 Formula 1 Ave
There is very small difference between the datasets, which cannot be
distinguished on the above chart.
Global Warming 1-2100 – Joseph Nowarski
7/11
Global Warming Land+Ocean Forecast
The forecast is based on four methods as described in publication [11].
All methods are for the business as usual CO2 mitigation scenario.
The methods are [11]:
o parabolic trendline of the last 61 years of global warming (GWTL)
o velocity and acceleration of global warming (GWA)
o parabolic trendline of the last 61 years of cumulated CO2 emissions (CO2TL)
o velocity and acceleration cumulative CO2 emissions (CO2A)
The average result from all four methods for the business as usual CO2 mitigation
scenario in 2100 is 4.4°C (4.1°C -5.0°C).
Chart 4 - Business as usual (BAU) GW forecast using all methods, °C
above 1850-1900 baseline, for land+ocean [11]
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
2020
2030
2040
2050
2060
2070
2080
2090
2100
GWTL GWA CO2TL CO2A Ave
The dataset applied in this work is Ave which is the average of all other datasets
considered in publications [11].
Global Warming 1-2100 – Joseph Nowarski
8/11
Global Warming over Land (GWL) and Global Warming over Ocean (GWO)
Forecast
The forecast is based on the optimal trendline as selected in publication [12].
All trendlines are for the business as usual CO2 mitigation scenario.
Table 7 - Selected option [12]
ID Option 4-1
Parameter GWO/GW
Trendline Power
from 1940
to 2021
k 0.992427705218
p -0.046868903112
R2 0.0576
GWL in 2030, °C 2.15
GWO in 2030, °C 1.20
GWL in 2100, °C 6.52
GWO in 2100, °C 3.45
Chart 5 - BAU forecast 2020-2100 using the selected trendline: Option
4-1: GWO/GW 1940-2021 Power Trendline, °C above pBL [12]
[13]
0.00
1.00
2.00
3.00
4.00
5.00
6.00
7.00
1940 1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100
GW GWL GWO
Global Warming 1-2100 – Joseph Nowarski
9/11
Global Warming 1-2100
Chart 6 - Global Warming 1-2100 °C above pBL
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GW GWL GWO
The dataset according to this work is publically available in publication [14] and
online on site nowagreen,com
Global Warming 1-2100 – Joseph Nowarski
10/11
References
1. IPCC - Changes in global surface temperature reconstructed from
paleoclimate archives (1-2000)
2. Kaufman, D., McKay, N., Routson, C. et al. Holocene global mean surface
temperature, a multi-method reconstruction approach. Sci Data 7, 201
(2020)
3. GISS Surface Temperature Analysis (GISTEMP), NASA Goddard Institute for
Space Studies
https://data.giss.nasa.gov/gistemp
4. Lenssen, N., G. Schmidt, J. Hansen, M. Menne, A. Persin, R. Ruedy, and D.
Zyss, 2019: Improvements in the GISTEMP uncertainty model. J. Geophys.
Res. Atmos., 124, no. 12, 6307-6326, doi:10.1029/2018JD029522
5. NOAA National Centers for Environmental Information - Climate at a Glance
https://www.ncei.noaa.gov/access/monitoring/climate-at-a-glance
6. Berkeley Earth - Global Warming - Global Temperature Data
https://berkeleyearth.org/data/
7. Rohde, R. A. and Hausfather, Z.: The Berkeley Earth Land/Ocean
Temperature Record, Earth Syst. Sci. Data, 12, 3469-3479, 2020,
https://doi.org/10.5194/essd-12-3469-2020
8. IPCC Sixth Assessment Report, Working Group 1
https://www.ipcc.ch/report/ar6/wg1/
Global Warming 1-2100 – Joseph Nowarski
11/11
9. Global Warming Datasets 1-2024 - Joseph Nowarski,
DOI:10.5281/zenodo.15013054
10. Global Warming Dataset Land+Ocean (1-2024), Land only (1750-2024),
Ocean only (1750-2024) – Joseph Nowarski, DOI:10.5281/zenodo.15013056
11. Global Warming Forecast Using Acceleration Factors - Joseph Nowarski,
DOI:10.5281/zenodo.6621042
12. Global Warming Forecast over the Land and over the Ocean - Joseph
Nowarski, DOI:10.5281/zenodo.14581167
13. Dataset Global Warming Forecast over the Land and over the Ocean -
Joseph Nowarski, DOI:10.5281/zenodo.14581169
14. Dataset Global Warming 1-2100 - Joseph Nowarski,
DOI:10.5281/zenodo.15034765
* * *

Supplementary resource (1)

Dataset Global Warming 1-2100
Data
March 2025
Joseph Nowarski
ResearchGate has not been able to resolve any citations for this publication.
The Berkeley Earth Land/Ocean Temperature Record
Article
Full-text available
  • Dec 2020
A global land–ocean temperature record has been created by combining the Berkeley Earth monthly land temperature field with spatially kriged version of the HadSST3 dataset. This combined product spans the period from 1850 to present and covers the majority of the Earth's surface: approximately 57 % in 1850, 75 % in 1880, 95 % in 1960, and 99.9 % by 2015. It includes average temperatures in 1∘×1∘ lat–long grid cells for each month when available. It provides a global mean temperature record quite similar to records from Hadley's HadCRUT4, NASA's GISTEMP, NOAA's GlobalTemp, and Cowtan and Way and provides a spatially complete and homogeneous temperature field. Two versions of the record are provided, treating areas with sea ice cover as either air temperature over sea ice or sea surface temperature under sea ice, the former being preferred for most applications. The choice of how to assess the temperature of areas with sea ice coverage has a notable impact on global anomalies over past decades due to rapid warming of air temperatures in the Arctic. Accounting for rapid warming of Arctic air suggests ∼ 0.1 ∘C additional global-average temperature rise since the 19th century than temperature series that do not capture the changes in the Arctic. Updated versions of this dataset will be presented each month at the Berkeley Earth website (http://berkeleyearth.org/data/, last access: November 2020), and a convenience copy of the version discussed in this paper has been archived and is freely available at 10.5281/zenodo.3634713 (Rohde and Hausfather, 2020).
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Holocene global mean surface temperature, a multi-method reconstruction approach
Article
Full-text available
  • Jun 2020
An extensive new multi-proxy database of paleo-temperature time series (Temperature 12k) enables a more robust analysis of global mean surface temperature (GMST) and associated uncertainties than was previously available. We applied five different statistical methods to reconstruct the GMST of the past 12,000 years (Holocene). Each method used different approaches to averaging the globally distributed time series and to characterizing various sources of uncertainty, including proxy temperature, chronology and methodological choices. The results were aggregated to generate a multi-method ensemble of plausible GMST and latitudinal-zone temperature reconstructions with a realistic range of uncertainties. The warmest 200-year-long interval took place around 6500 years ago when GMST was 0.7 °C (0.3, 1.8) warmer than the 19th Century (median, 5th, 95th percentiles). Following the Holocene global thermal maximum, GMST cooled at an average rate −0.08 °C per 1000 years (−0.24, −0.05). The multi-method ensembles and the code used to generate them highlight the utility of the Temperature 12k database, and they are now available for future use by studies aimed at understanding Holocene evolution of the Earth system.
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Improvements in the GISTEMP Uncertainty Model
Article
Full-text available
  • Jun 2019
We outline a new and improved uncertainty analysis for the Goddard Institute for Space Studies Surface Temperature product version 4 (GISTEMP v4). Historical spatial variations in surface temperature anomalies are derived from historical weather station data and ocean data from ships, buoys, and other sensors. Uncertainties arise from measurement uncertainty, changes in spatial coverage of the station record, and systematic biases due to technology shifts and land cover changes. Previously published uncertainty estimates for GISTEMP included only the effect of incomplete station coverage. Here, we update this term using currently available spatial distributions of source data, state‐of‐the‐art reanalyses, and incorporate independently derived estimates for ocean data processing, station homogenization, and other structural biases. The resulting 95% uncertainties are near 0.05 °C in the global annual mean for the last 50 years and increase going back further in time reaching 0.15 °C in 1880. In addition, we quantify the benefits and inherent uncertainty due to the GISTEMP interpolation and averaging method. We use the total uncertainties to estimate the probability for each record year in the GISTEMP to actually be the true record year (to that date) and conclude with 87% likelihood that 2016 was indeed the hottest year of the instrumental period (so far).
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